This document provides an overview of the piXserve solution for intelligent image and video search capabilities. piXserve can automatically index and analyze image and video content to generate searchable metadata without manual intervention. It allows users to perform visual searches using images, video frames, objects, faces, text, or complex multi-modal queries. piXserve also enables powerful automated alerting when matches to specified criteria are found. The solution is scalable and can process live and archived video and image sources for applications in defense, security and intelligence.
DEEPFAKE DETECTION TECHNIQUES: A REVIEWvivatechijri
Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
This document discusses Generative Adversarial Networks (GANs) and their applications. GANs use two neural networks, a generator and discriminator, that compete against each other in a game theoretic framework. The generator learns to generate new data instances to fool the discriminator, while the discriminator learns to assess examples as real or generated. GANs have been used to generate realistic images, videos and more. However, training GANs is challenging and they lack interpretability. The document provides an overview of GAN concepts and applications, with tips for building and training effective GAN models.
JISC Observatory: Horizon Scanning for Higher and Further EducationThom Bunting
Explanation at IWMW 2012 of JISC Observatory's method and on-going work in horizon-scanning for technical innovation of relevance to Higher Education and Further Education. This presentation highlights the development process for two forthcoming TechWatch reports: 1) Preparing for Data-driven Infrastructure; 2) Preparing for Effective Adoption and Use of eBooks in Education).
The User at the Wheel of the Online Video Search Engineckofler
The recent increase in the volume and variety of video content available online presents growing challenges for video search. Users face increased difficulty in formulating effective queries and search engines must deploy highly effective algorithms to provide relevant results. This talk addresses these challenges by introducing two novel frameworks and approaches. First, we discuss a principled framework for multimedia retrieval that moves beyond 'what' users are searching for also to encompass 'why' they search. This 'why' is understood as the reason, purpose or immediate goal behind a user information need, which is identified as the underlying 'user intent'. We identify useful intent categories for online video search, present validation experiments showing that these categories display enough invariance to be successfully modeled by a video search engine and demonstrate the potential for these categories to improve video retrieval with a large crowdsourcing user study. Second, we present a novel approach able to predict for which queries results optimization is most useful, i.e., predicting which queries will fail in the search session of a user on a video search engine. Being able to predict when a video search query would fail is likely to make the video search result optimization more efficient and deploy optimization techniques more effectively. This approach uses a combination of features derived from the search log of a video search engine (capturing users' behavior) and features derived from the video search results list (capturing the visual variance of search results), with the objective to predict whether a particular query is likely to fail in the context of a particular search session.
JISC Observatory: Horizon Scanning for Higher & Further EducationThom Bunting
Explanation at IWMW 2012 of JISC Observatory's method and on-going work in horizon-scanning for technical innovation of relevance to Higher Education and Further Education. This presentation highlights the development process for two forthcoming TechWatch reports: 1) Preparing for Data-driven Infrastructure; 2) Preparing for Effective Adoption and Use of eBooks in Education).
Identifying and Responding to Emerging Technologieslisbk
Slides for a talk on "Identifying and Responding to Emerging Technologies" to be given by Brian Kelly, UKOLN at the IWMW 2012 event to be held in Edinburgh on 18-20 June 2012.
See http://iwmw.ukoln.ac.uk/iwmw2012/sessions/jisc-observatory/
Aria Armaghan Afghan General Trading L.L.CMr_diamond
Aria Armaghan Afghan General Trading L.L.C is a Non- Governmental Company owned and operating by Afghans, registered with Ministry of Commerce Afghanistan and affiliated of Number of Local & International prominent companies Under License Number Is 18879
Aria Armaghan Afghan General Trading L.L.C is a global logistics & trading company providing integrated transportation solutions to a wide array of Customers, with offices nationwide and strategically located warehouse and distribution centres.
Throughout Afghanistan Aria Armaghan Afghan General Trading L.L.C is well positioned to accommodate your needs.
Globally, UAS are used for ISR missions but over 99% of collected image and video content remains unexploited in real-time due to lack of automated analytics. piXlogic offers an image and video analytics platform called piXserve that can automatically index, classify, match, and tag objects of interest in real-time to produce actionable intelligence during missions. piXserve allows targeted video clips to be sent to ground stations over bandwidth-limited links to enable immediate action.
DEEPFAKE DETECTION TECHNIQUES: A REVIEWvivatechijri
Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
This document discusses Generative Adversarial Networks (GANs) and their applications. GANs use two neural networks, a generator and discriminator, that compete against each other in a game theoretic framework. The generator learns to generate new data instances to fool the discriminator, while the discriminator learns to assess examples as real or generated. GANs have been used to generate realistic images, videos and more. However, training GANs is challenging and they lack interpretability. The document provides an overview of GAN concepts and applications, with tips for building and training effective GAN models.
JISC Observatory: Horizon Scanning for Higher and Further EducationThom Bunting
Explanation at IWMW 2012 of JISC Observatory's method and on-going work in horizon-scanning for technical innovation of relevance to Higher Education and Further Education. This presentation highlights the development process for two forthcoming TechWatch reports: 1) Preparing for Data-driven Infrastructure; 2) Preparing for Effective Adoption and Use of eBooks in Education).
The User at the Wheel of the Online Video Search Engineckofler
The recent increase in the volume and variety of video content available online presents growing challenges for video search. Users face increased difficulty in formulating effective queries and search engines must deploy highly effective algorithms to provide relevant results. This talk addresses these challenges by introducing two novel frameworks and approaches. First, we discuss a principled framework for multimedia retrieval that moves beyond 'what' users are searching for also to encompass 'why' they search. This 'why' is understood as the reason, purpose or immediate goal behind a user information need, which is identified as the underlying 'user intent'. We identify useful intent categories for online video search, present validation experiments showing that these categories display enough invariance to be successfully modeled by a video search engine and demonstrate the potential for these categories to improve video retrieval with a large crowdsourcing user study. Second, we present a novel approach able to predict for which queries results optimization is most useful, i.e., predicting which queries will fail in the search session of a user on a video search engine. Being able to predict when a video search query would fail is likely to make the video search result optimization more efficient and deploy optimization techniques more effectively. This approach uses a combination of features derived from the search log of a video search engine (capturing users' behavior) and features derived from the video search results list (capturing the visual variance of search results), with the objective to predict whether a particular query is likely to fail in the context of a particular search session.
JISC Observatory: Horizon Scanning for Higher & Further EducationThom Bunting
Explanation at IWMW 2012 of JISC Observatory's method and on-going work in horizon-scanning for technical innovation of relevance to Higher Education and Further Education. This presentation highlights the development process for two forthcoming TechWatch reports: 1) Preparing for Data-driven Infrastructure; 2) Preparing for Effective Adoption and Use of eBooks in Education).
Identifying and Responding to Emerging Technologieslisbk
Slides for a talk on "Identifying and Responding to Emerging Technologies" to be given by Brian Kelly, UKOLN at the IWMW 2012 event to be held in Edinburgh on 18-20 June 2012.
See http://iwmw.ukoln.ac.uk/iwmw2012/sessions/jisc-observatory/
Aria Armaghan Afghan General Trading L.L.CMr_diamond
Aria Armaghan Afghan General Trading L.L.C is a Non- Governmental Company owned and operating by Afghans, registered with Ministry of Commerce Afghanistan and affiliated of Number of Local & International prominent companies Under License Number Is 18879
Aria Armaghan Afghan General Trading L.L.C is a global logistics & trading company providing integrated transportation solutions to a wide array of Customers, with offices nationwide and strategically located warehouse and distribution centres.
Throughout Afghanistan Aria Armaghan Afghan General Trading L.L.C is well positioned to accommodate your needs.
Globally, UAS are used for ISR missions but over 99% of collected image and video content remains unexploited in real-time due to lack of automated analytics. piXlogic offers an image and video analytics platform called piXserve that can automatically index, classify, match, and tag objects of interest in real-time to produce actionable intelligence during missions. piXserve allows targeted video clips to be sent to ground stations over bandwidth-limited links to enable immediate action.
IRJET- Real-Time Object Detection System using Caffe ModelIRJET Journal
This document discusses a real-time object detection system using the Caffe model. The authors used OpenCV, Caffe model, Python and NumPy to build a system that can detect objects like humans and vehicles in images and videos. It discusses how deep learning techniques like convolutional neural networks can be used for tasks like object localization, classification and feature extraction. Specifically, it explores using the Caffe framework to implement real-time object detection with OpenCV by accessing the webcam and applying detection to each frame.
IRJET- Object Detection in an Image using Deep LearningIRJET Journal
The document summarizes object detection in images using deep learning. It introduces common object detection methods like convolutional neural networks (CNNs) and regional-based CNNs. CNNs are effective for object detection as they can automatically learn distinguishing features without needing manually defined features. The document then describes the methodology which uses a CNN with layers like convolution, ReLU, pooling and fully connected layers to perform feature extraction and classification. It concludes that CNNs provide an efficient method for real-time object detection and segmentation in images through deep learning.
Deep learning, in particular, is the new frontier for the video industry, allowing video professionals to do things automatically that would have taken weeks of work in the past, as well as some things that wouldn't have been possible at all. How is deep learning different from other machine learning algorithms? And what are its practical applications for broadcasting and filmed entertainment? What are the science and its business ramifications?
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...Dejan Kovachev
This document presents XMMC, a real-time collaboration-enabled mobile augmented reality system with semantic multimedia. XMMC allows experts to collaboratively document cultural heritage sites using multimedia annotations and metadata. It uses an XMPP-based architecture to enable real-time sharing of multimedia and annotations between mobile clients. Concurrent editing of XML metadata is supported using an adaptation of the CEFX+ algorithm. An XMPP-extended augmented reality browser integrates multimedia annotations and metadata into a live video stream. Evaluation shows XMMC supports the collaborative documentation workflow while increasing cultural heritage awareness.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
This document describes an innovative unified video surveillance system (Unified VSS) developed by Networking For Future (NFF) to address challenges with analyzing and storing video data from multiple disconnected legacy surveillance systems. The Unified VSS uses a "Red Zone" to capture video streams from different systems and a "Green Zone" with video management and analytics software to analyze and view the stored video. This platform provides a centralized storage solution with greater retention capabilities and analytics compared to existing systems, allowing organizations to consolidate video from multiple sources.
1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
IRJET- Object Detection in Real Time using AI and Deep LearningIRJET Journal
This document summarizes research on object detection techniques using AI and deep learning. It discusses how object detection can be used to enhance e-commerce by recommending products seen in videos. The document reviews several existing object detection algorithms and methods, including YOLO and topological maps. It also identifies limitations in existing systems for multi-class detection efficiency and handling similar backgrounds. The researchers propose using object detection with video input for product recommendations and improving current systems.
This document summarizes a proposed method for text-based video retrieval. The method involves:
1) Extracting frames from videos and segmenting text regions within frames.
2) Recognizing characters using optical character recognition (OCR) and extracting color features.
3) Storing the text features and color features in a database.
4) Matching user-inputted text queries to the stored text features to retrieve matching videos. The proposed method aims to improve video indexing and retrieval accuracy compared to visual query methods.
Query clip genre recognition using tree pruning technique for video retrievalIAEME Publication
The document proposes a method for video retrieval based on genre recognition of a query video clip. It extracts regions of interest from frames of the query clip and videos in a database based on motion detection. Features are extracted from these regions and used for matching to recognize the genre. A tree pruning technique is employed to identify the genre of the query clip and retrieve similar genre videos from the database. The method segments objects, recognizes them, and uses tree pruning for genre recognition and retrieval. It was evaluated on a dataset containing sports, movies, and news genres and showed effectiveness in genre recognition and retrieval.
Query clip genre recognition using tree pruning technique for video retrievalIAEME Publication
The document proposes a method for video retrieval based on genre recognition of a query video clip. It extracts regions of interest from frames of the query clip and videos in a database. Features are extracted from these regions and used for matching via Euclidean distance. A tree pruning technique is employed to recognize the genre of the query clip and retrieve similar genre videos from the database. The method segments objects, extracts features, performs matching and genre recognition, and retrieves relevant videos in three or fewer sentences.
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal
This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
What to curate? Preserving and Curating Software-Based Artneilgrindley
This is a presentation given at the CHArt (Computers and History of Art) conference held in London in November 2011. The slides on the title page are images taken from works exhibited at the V&A Decode exhibition.
Content Modelling for Human Action Detection via Multidimensional ApproachCSCJournals
Video content analysis is an active research domain due to the availability and the increment of audiovisual data in the digital format. There is a need to automatically extracting video content for efficient access, understanding, browsing and retrieval of videos. To obtain the information that is of interest and to provide better entertainment, tools are needed to help users extract relevant content and to effectively navigate through the large amount of available video information. Existing methods do not seem to attempt to model and estimate the semantic content of the video. Detecting and interpreting human presence, actions and activities is one of the most valuable functions in this proposed framework. The general objectives of this research are to analyze and process the audio-video streams to a robust audiovisual action recognition system by integrating, structuring and accessing multimodal information via multidimensional retrieval and extraction model. The proposed technique characterizes the action scenes by integrating cues obtained from both the audio and video tracks. Information is combined based on visual features (motion, edge, and visual characteristics of objects), audio features and video for recognizing action. This model uses HMM and GMM to provide a framework for fusing these features and to represent the multidimensional structure of the framework. The action-related visual cues are obtained by computing the spatiotemporal dynamic activity from the video shots and by abstracting specific visual events. Simultaneously, the audio features are analyzed by locating and compute several sound effects of action events that embedded in the video. Finally, these audio and visual cues are combined to identify the action scenes. Compared with using single source of either visual or audio track alone, such combined audiovisual information provides more reliable performance and allows us to understand the story content of movies in more detail. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
IRJET- Object Detection and Recognition for Blind AssistanceIRJET Journal
1. The document proposes a system using object and color recognition and convolutional neural networks to enhance the capabilities of visually impaired people.
2. The system uses a camera mounted on glasses to capture images which are then preprocessed, compressed, and used to train a classifier model to recognize common objects.
3. The proposed hardware implementation uses a Raspberry Pi for its small size and open source software support, including TensorFlow for training convolutional neural network models.
Automatic Visual Concept Detection in Videos: ReviewIRJET Journal
This document reviews various methods for automatic visual concept detection in videos. It discusses three main areas of concept learning: event analysis, attribute concept detection, and knowledge transfer. For event analysis, it describes methods like Domain Selection Machine (DSM) and learning from natural language descriptions. It also reviews works on attribute detection like unseen object classification. For knowledge transfer, it discusses domain adaptation and object/scene collection methods. The paper concludes that developing an automatic system for concept detection from video text could provide an efficient approach with minimal user interaction.
This document discusses the development of a face mask detection system using YOLOv4. The system uses a deep learning model with YOLOv4 to detect faces in real-time video and determine if each person is wearing a mask or not. It is trained on images of faces with and without masks. The model uses CSPDarknet53 as the backbone network and PANet for feature aggregation. It is implemented with OpenCV and a Python GUI for a user interface. The goal is to help enforce mask mandates and alert authorities if too many people in an area are not wearing masks.
This document discusses democratizing AI using Apache Spark. It summarizes that while AI is advancing rapidly, it has not been fully democratized due to challenges with data management, developing productive teams, and establishing production-ready applications. Databricks aims to close these gaps with its just-in-time data platform that provides integrated workspaces, automated Spark management, and supports deep learning use cases across industries.
Voice Enable Blind Assistance System -Real time Object DetectionIRJET Journal
This document describes a voice-enabled blind assistance system using real-time object detection. The system uses a Single Shot Multi-Box Detection (SSD) model with MobileNet to detect objects in frames captured by a webcam in real-time. When an object is detected, its class is converted to speech using text-to-speech and provided to blind users along with alerts if the object is too close. The system aims to help visually impaired people gain more independence by identifying objects and hazards in their environment. An experiment showed the SSD MobileNet model achieved accurate real-time detection of household objects.
Mobile Visual Search (MVS) allows users to search for visual data like images and videos using mobile devices. It faces challenges of low latency, limited computational power and bandwidth. The basic MVS pipeline extracts features from a query image, finds similar images in a database, and verifies matches. Research opportunities exist to improve interactive search under mobile constraints and enable robust recognition with varying image conditions. Datasets are also needed to advance MVS research.
IRJET- Real-Time Object Detection System using Caffe ModelIRJET Journal
This document discusses a real-time object detection system using the Caffe model. The authors used OpenCV, Caffe model, Python and NumPy to build a system that can detect objects like humans and vehicles in images and videos. It discusses how deep learning techniques like convolutional neural networks can be used for tasks like object localization, classification and feature extraction. Specifically, it explores using the Caffe framework to implement real-time object detection with OpenCV by accessing the webcam and applying detection to each frame.
IRJET- Object Detection in an Image using Deep LearningIRJET Journal
The document summarizes object detection in images using deep learning. It introduces common object detection methods like convolutional neural networks (CNNs) and regional-based CNNs. CNNs are effective for object detection as they can automatically learn distinguishing features without needing manually defined features. The document then describes the methodology which uses a CNN with layers like convolution, ReLU, pooling and fully connected layers to perform feature extraction and classification. It concludes that CNNs provide an efficient method for real-time object detection and segmentation in images through deep learning.
Deep learning, in particular, is the new frontier for the video industry, allowing video professionals to do things automatically that would have taken weeks of work in the past, as well as some things that wouldn't have been possible at all. How is deep learning different from other machine learning algorithms? And what are its practical applications for broadcasting and filmed entertainment? What are the science and its business ramifications?
A Real-time Collaboration-enabled Mobile Augmented Reality System with Semant...Dejan Kovachev
This document presents XMMC, a real-time collaboration-enabled mobile augmented reality system with semantic multimedia. XMMC allows experts to collaboratively document cultural heritage sites using multimedia annotations and metadata. It uses an XMPP-based architecture to enable real-time sharing of multimedia and annotations between mobile clients. Concurrent editing of XML metadata is supported using an adaptation of the CEFX+ algorithm. An XMPP-extended augmented reality browser integrates multimedia annotations and metadata into a live video stream. Evaluation shows XMMC supports the collaborative documentation workflow while increasing cultural heritage awareness.
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
This document describes an innovative unified video surveillance system (Unified VSS) developed by Networking For Future (NFF) to address challenges with analyzing and storing video data from multiple disconnected legacy surveillance systems. The Unified VSS uses a "Red Zone" to capture video streams from different systems and a "Green Zone" with video management and analytics software to analyze and view the stored video. This platform provides a centralized storage solution with greater retention capabilities and analytics compared to existing systems, allowing organizations to consolidate video from multiple sources.
1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
IRJET- Object Detection in Real Time using AI and Deep LearningIRJET Journal
This document summarizes research on object detection techniques using AI and deep learning. It discusses how object detection can be used to enhance e-commerce by recommending products seen in videos. The document reviews several existing object detection algorithms and methods, including YOLO and topological maps. It also identifies limitations in existing systems for multi-class detection efficiency and handling similar backgrounds. The researchers propose using object detection with video input for product recommendations and improving current systems.
This document summarizes a proposed method for text-based video retrieval. The method involves:
1) Extracting frames from videos and segmenting text regions within frames.
2) Recognizing characters using optical character recognition (OCR) and extracting color features.
3) Storing the text features and color features in a database.
4) Matching user-inputted text queries to the stored text features to retrieve matching videos. The proposed method aims to improve video indexing and retrieval accuracy compared to visual query methods.
Query clip genre recognition using tree pruning technique for video retrievalIAEME Publication
The document proposes a method for video retrieval based on genre recognition of a query video clip. It extracts regions of interest from frames of the query clip and videos in a database based on motion detection. Features are extracted from these regions and used for matching to recognize the genre. A tree pruning technique is employed to identify the genre of the query clip and retrieve similar genre videos from the database. The method segments objects, recognizes them, and uses tree pruning for genre recognition and retrieval. It was evaluated on a dataset containing sports, movies, and news genres and showed effectiveness in genre recognition and retrieval.
Query clip genre recognition using tree pruning technique for video retrievalIAEME Publication
The document proposes a method for video retrieval based on genre recognition of a query video clip. It extracts regions of interest from frames of the query clip and videos in a database. Features are extracted from these regions and used for matching via Euclidean distance. A tree pruning technique is employed to recognize the genre of the query clip and retrieve similar genre videos from the database. The method segments objects, extracts features, performs matching and genre recognition, and retrieves relevant videos in three or fewer sentences.
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal
This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
What to curate? Preserving and Curating Software-Based Artneilgrindley
This is a presentation given at the CHArt (Computers and History of Art) conference held in London in November 2011. The slides on the title page are images taken from works exhibited at the V&A Decode exhibition.
Content Modelling for Human Action Detection via Multidimensional ApproachCSCJournals
Video content analysis is an active research domain due to the availability and the increment of audiovisual data in the digital format. There is a need to automatically extracting video content for efficient access, understanding, browsing and retrieval of videos. To obtain the information that is of interest and to provide better entertainment, tools are needed to help users extract relevant content and to effectively navigate through the large amount of available video information. Existing methods do not seem to attempt to model and estimate the semantic content of the video. Detecting and interpreting human presence, actions and activities is one of the most valuable functions in this proposed framework. The general objectives of this research are to analyze and process the audio-video streams to a robust audiovisual action recognition system by integrating, structuring and accessing multimodal information via multidimensional retrieval and extraction model. The proposed technique characterizes the action scenes by integrating cues obtained from both the audio and video tracks. Information is combined based on visual features (motion, edge, and visual characteristics of objects), audio features and video for recognizing action. This model uses HMM and GMM to provide a framework for fusing these features and to represent the multidimensional structure of the framework. The action-related visual cues are obtained by computing the spatiotemporal dynamic activity from the video shots and by abstracting specific visual events. Simultaneously, the audio features are analyzed by locating and compute several sound effects of action events that embedded in the video. Finally, these audio and visual cues are combined to identify the action scenes. Compared with using single source of either visual or audio track alone, such combined audiovisual information provides more reliable performance and allows us to understand the story content of movies in more detail. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
IRJET- Object Detection and Recognition for Blind AssistanceIRJET Journal
1. The document proposes a system using object and color recognition and convolutional neural networks to enhance the capabilities of visually impaired people.
2. The system uses a camera mounted on glasses to capture images which are then preprocessed, compressed, and used to train a classifier model to recognize common objects.
3. The proposed hardware implementation uses a Raspberry Pi for its small size and open source software support, including TensorFlow for training convolutional neural network models.
Automatic Visual Concept Detection in Videos: ReviewIRJET Journal
This document reviews various methods for automatic visual concept detection in videos. It discusses three main areas of concept learning: event analysis, attribute concept detection, and knowledge transfer. For event analysis, it describes methods like Domain Selection Machine (DSM) and learning from natural language descriptions. It also reviews works on attribute detection like unseen object classification. For knowledge transfer, it discusses domain adaptation and object/scene collection methods. The paper concludes that developing an automatic system for concept detection from video text could provide an efficient approach with minimal user interaction.
This document discusses the development of a face mask detection system using YOLOv4. The system uses a deep learning model with YOLOv4 to detect faces in real-time video and determine if each person is wearing a mask or not. It is trained on images of faces with and without masks. The model uses CSPDarknet53 as the backbone network and PANet for feature aggregation. It is implemented with OpenCV and a Python GUI for a user interface. The goal is to help enforce mask mandates and alert authorities if too many people in an area are not wearing masks.
This document discusses democratizing AI using Apache Spark. It summarizes that while AI is advancing rapidly, it has not been fully democratized due to challenges with data management, developing productive teams, and establishing production-ready applications. Databricks aims to close these gaps with its just-in-time data platform that provides integrated workspaces, automated Spark management, and supports deep learning use cases across industries.
Voice Enable Blind Assistance System -Real time Object DetectionIRJET Journal
This document describes a voice-enabled blind assistance system using real-time object detection. The system uses a Single Shot Multi-Box Detection (SSD) model with MobileNet to detect objects in frames captured by a webcam in real-time. When an object is detected, its class is converted to speech using text-to-speech and provided to blind users along with alerts if the object is too close. The system aims to help visually impaired people gain more independence by identifying objects and hazards in their environment. An experiment showed the SSD MobileNet model achieved accurate real-time detection of household objects.
Mobile Visual Search (MVS) allows users to search for visual data like images and videos using mobile devices. It faces challenges of low latency, limited computational power and bandwidth. The basic MVS pipeline extracts features from a query image, finds similar images in a database, and verifies matches. Research opportunities exist to improve interactive search under mobile constraints and enable robust recognition with varying image conditions. Datasets are also needed to advance MVS research.
1. A piXlogic White Paper
Sponsored by Flex Analytics
4984 El Camino Real
Suite 205
Los Altos, CA 94022
T. 650-967-4067
info@piXlogic.com
www.piXlogic.com
Intelligent Image and Video Search
for Defense Applications
Government Reseller for piXlogic
10314 Thornbush Lane
Bethesda, MD 20814
(301) 787-2989
Jul-2012
3. Contents Introduction
Images and videos have always been key
Introduction 3 elements of intelligence and defense
Problem Statement 3 operations. In recent years, the scope and
diversity of digital imagery has greatly
Previous Options 4
increased in every area: ground, satellite,
The piXserve Solution 5 UAV, surveillance, broadcast, etc. The
Key Features of piXserve 5 volume of material being acquired and
Security Applications 8 stored is staggering, with no visible plateau
in sight. Traditional methods of organizing,
Implementation 9
cataloguing, and distributing this material to
Summary 9 analysts and the war-fighter are becoming
About piXlogic 10 impractical due to the scale involved. On
the other hand, timely access to nuggets of
vital information contained in images/videos
is key to operational success. The ability to
cross-correlate the information, whether it’s
being obtained from live sources or from
archived repositories, is more important than
ever.
In this environment, image/video search and
retrieval has become the new “must have”
element of any comprehensive solution.
Unfortunately, today’s image/video
management systems are not well suited to
help make sense of the data collected, and
can only provide, at best, very limited search
and retrieval capabilities.
Problem Statement
Most video management systems offer
limited options for automating processes
such as searching archived footage, or
generating alerts from live video. For the
most part, these features are either not
available, or only available in a very limited
sense. Often, a significant amount of
manpower is required to carry out even
simple search tasks. This is well known in
the field. Correlating visual data from
July 2012 pg. 3
4. different sources is another very generally in the same location on the image.
challenging task, mostly done Both of these requirements limit the scope
manually today. Automated change of applications possible with such systems.
detection is yet another largely
elusive goal. Face Recognition: Much as in the ALPR
case, a big hurdle is to know where the face
Industry/government efforts during to be measured is on the image. To solve
the last few years have focused on this, typical systems require that the distance
building infrastructure and have between the camera and the subject be
resulted in great improvements in the within a predefined range. Lighting
ability to acquire higher resolution variations are also critical which is why the
imagery/full motion video, moving more successful implementations are limited
this material around the network to indoor, entry-way, type of set-ups.
efficiently, and storing it. These are Outdoor video in unconstrained
great accomplishments, but by environments presents a challenge that is
themselves they are not enough. outside the realm of most commercial
Now is the time to leverage previous solutions available today.
investments and provide a much
needed level of automation so that Object Detection: The ability to
analysts can deal with the size and detect/recognize/search for specific objects
scale of the problems they face. in a video or an image is not usually
However, for most solution available. Some attempts have been made
providers, this remains a significant for video, but the methods used are overly
technical challenge. simplistic and unreliable. A typical
technique relies on “frame differencing” to
Previous Options separate moving things from a stationary
When automated video analysis tools background. The idea is simple but
are available, they tend to be single- unfortunately it only works in trivial
purpose with limited scope of situations. If the camera is moving, the
applicability and stringent operating background will move as well and frame
requirements. Consider the differencing techniques won’t work.
following three examples: Turning off a light, a cloud passing in the
sky, a moving shadow, these are all things
Automated License Plate that can yield undesired results. Even when
Recognition: For most systems, the the background and the camera are
hurdle is to know where the license stationary, the amount of information that is
plate is in the image being analyzed. obtained is limited. If the camera is
To circumvent this problem, solution calibrated, some guess about the size of the
providers either require the use of object can be made and from this an
specialized cameras (infrared) or that inference can be derived about what is in the
the cameras be placed such that the scene (perhaps an adult, may be not a dog),
license plate to be recognized is but even this too can be quite unreliable (is
July 2012 pg. 4
5. it a dog, a tumbleweed, or a far away
person). Crowded environments
present a critical challenge to today’s 2. piXserve reasons about what it "saw"
systems. in the image and develops an initial
level of "understanding" about
content and context. Where it can, it
The piXserve Solution automatically creates searchable
piXserve is a general-purpose "tags" for what it saw in the image
image/video search and alerting (piXlogic calls these tags "Notions").
solution. Breakthrough technology For example, it can detect the
developed by piXlogic allows the presence of things such as: sky,
software to automatically “see” the vegetation, flower, face, building,
contents of an image/video frame car, map, airplane, helicopter, etc.
and create a searchable index and
uses this information so that users 3. piXserve uses all the information
can search and create alerts in a very calculated from the image to make
natural and logical way. comparisons between a search image
and previously indexed
1. piXserve automatically images/videos so that users can find
“segments” an image in a results that most closely match what
way that discerns the they are looking for.
individual objects in the
image. It creates a 4. piXserve can "see" not only visual
mathematical description of objects but also text strings that may
the appearance of these appear anywhere in the field of view
objects "on the fly", and of the image. This text is also
stores it as a searchable index indexed and made searchable.
in a database. piXserve works with text from many
languages (alphanumeric/latin-
character based languages, Japanese,
Korean, Chinese, etc.)
5. Depending on the quality of the
imagery involved and the type of
search being done, piXserve has
been designed to achieve accuracies
in excess of 85%.
Key Features of piXserve
Automatic Indexing
Point piXserve to a repository of
July 2012 pg. 5
6. images/video files or to a live the mouse to point to an area of
video feed, and automatically the query image to indicate
index content. No manual which specific item(s) should
intervention or data entry be searched for.
required.
3. Browse the contents of existing
Powerful Search databases, grab a frame “on the
Through a web browser fly” from a video that is
interface, users login to playing, and use that frame to
piXserve, connect to formulate a visual search
available databases and query.
formulate search queries to
retrieve desired images/video 4. Search images and videos by
segments: object class ("Notion")
1. Use an arbitrary image 5. Type a text string to search
to search for pictures/videos where that
images/video segments string appears in the field of
that contain the same or view (a license plate, a street
similar items sign, a name tag, etc.)
6. Search for faces of specific
individuals
7. Perform not only simple but
also complex multi-modal
searches. (Example: find video
sequences where something
like the bag in this picture
AND this face from this other
picture AND this text string I
just typed all appear in the field
of view at the same time.)
Use AND, OR, and NOT
operators to combine up to
6 criteria in a single query.
2. U
s
e
July 2012 pg. 6
7. 8. Search by file name and naming)
9. Search by keyword or Powerful Alerts
other external metadata, Create alert criteria just as you would
if available. formulate a search query. piXserve-
ALERT keeps track of what
10. Submit sample images piXserve machines on the network
of non-deformable are indexing and when a match is
objects of interest and made consistent with what the user
automatically tag specified, it generates a signal. The
images/video frames user receives an e-mail with a link to
when these items are the alert results. A JMS (Java
visible. Messaging Service) signal is also
generated to pass the alert on to other
Powerful Automated Tagging systems and applications for further
action.
1. Automatically tag
images/video frames Powerful Metadata
with the name of The richness of metadata calculated
recognized by piXserve about each image/video
individuals that frame processed (objects and tags),
appear therein can be exploited to enable
(automated face customized applications that are of
naming). high value in a variety of settings
such as:
2. Suggest keywords to
describe the contents 1. Automatic determination of
of a picture/video change detection when
frame (automated videos taken at different
keyword times from different angles
recommendations) are compared.
2. Determining which portions
3. Submit sample of a video archive contain
images of non- useful information, and
deformable objects of which could be safely deleted
interest and to minimize storage
automatically tag requirements.
images/video frames
when these items are Scaleable Architecture
visible. (automated piXserve is a multi-threaded, J2EE
2D-object detection scalable application that is suitable
for the most demanding
July 2012 pg. 7
8. implementations. piXserve extends the capabilities of today’s
systems by adding the ability to
Web Services API automatically analyze the video that is being
A REST-based API package collected and stored. These video streams
is available to support can be intercepted by piXserve and analyzed
integrations with third party for alerting purposes. Similarly, recorded
applications and workflow video can be analyzed, searched and
environments. correlated using piXserve. The analytical
capabilities in piXserve support: facial
Security Applications recognition, general purpose object
If you are concerned with the cost, detection and recognition, text recognition,
speed, and accuracy of your video license plate recognition, automatic tagging,
investigative work, whether it be and more. All the indexing work is done
forensic in nature or dealing with automatically, server side, in the
live situations, then you should background. Users are then free to create
consider piXserve as a “must-have” visually-based search criteria and navigate
add-on to your current system. the body of accumulated material. They can
do all of this “on the fly”, as they see fit at
Conventional systems focus on the moment, based on whatever problem or
managing and manipulating cameras situation they are dealing with.
and storage devices. Unfortunately,
they only provide limited capabilities The piXserve search environment is
for searching the captured video: intuitive and productive, and the user
time, date, motion, transaction interface is through a web browser (Internet
trigger…these are among the more Explorer, Mozilla Firefox, Safari, Google
common set of options available. Chrome, or equivalent). Users can drag-
While useful, these features alone are and-drop a picture from anywhere to
inadequate to support a productive formulate a similarity search query, or pause
workflow and significant manpower a video while it’s playing, and use that
effort is required even for the simpler frame to create a new search criteria or
tasks. Common situations involve refine an existing one. This latter capability
several operators having to stare at a greatly simplifies the discovery process
bank of monitors for hours on end in precisely in those situations when the user
order to catch an event of interest, or isn’t quite sure what they are looking for and
having to wade through hundreds of are working in an investigative/exploratory
hours of video from many cameras mode.
looking for a specific event or trying
to correlate separate ones. These
situations are labor intensive, error
prone, and do not scale well.
July 2012 pg. 8
9. Implementation
piXserve can process videos in a
variety of formats (MPEG-1, MPEG-
2, MPEG-4, H-263, H-264, etc.).
piXserve can also process still
images in over 90 different formats
(jpeg, tiff, png, bmp, psd, etc.)
piXserve can index both archived
video as well as live video broadcast
from Multicast IP cameras. piXserve
and piXserve-ALERT run on
standard 2-CPU rack servers (multi-
core Intel-Xeon processors or
equivalent), in a Windows Server
2003 or 2008 environment.
Customers typically choose Dell or
HP hardware for implementation. parallelize throughput and serve growing
piXserve is available in both x32 and needs.
x64 bit versions.
In order to index archived video The metadata created by piXserve is stored
piXserve requires that the storage in an RDBMs (Oracle or MS-SQL are
device be accessible via a network supported, PostgreSQL is bundled with
share (Linux/Unix/Windows). piXserve). The data and the piXserve output
Further, the stored video should not can be integrated/correlated to that from
be in a proprietary, non-standard other systems that the customer may be
format. using. The alerting functionality is provided
by piXserve-ALERT. A single instance of
A single server can process large piXserve-ALERT can serve many users and
amounts of archived material, or live monitor potentially thousands of alert
video from multiple feeds/sources. criteria. Here too scaling is achieved by
The higher the number of cores on adding additional ALERT servers. In
the server, the higher the number of configurations were several hundreds or
hours of video per day that can be thousands of individuals will be searching
processed by a single machine. piXserve generated data, the use of
piXserve implementations can range piXserve-Enterprise Edition is
in size, from as little as a single recommended.
server to scalable multi-server and
distributed configurations. The Summary
architecture of the product is such Images and videos are a critical element of
that as the needs of the customer defense and intelligence operations. It is
grow, hardware can be added to very difficult to deal with an ever-growing
amount of captured video without
July 2012 pg. 9
10. automation. The alternatives to Content Auto-tagging (automatically
automation are expensive, time label an image/video)
consuming, and prone to errors. At Content Alerting (automatically inform
the same time, there is a lack of users when items of interest appear in a
suitable tools to provide a
live video stream or web crawl)
meaningful level of real-world
automation. Content Change Detection
(automatically compare images and
piXserve provides an unparalleled video segments to detect changes at
level of image analysis and the object level)
understanding. In a single tool it
provides capabilities that span: piXlogic serves the needs of government
object detection and recognition, and industrial customers. piXlogic sells its
face recognition, license plate products directly and through a network of
recognition, text recognition, resellers in the US, the UK, Japan, Australia,
automatic tagging, and more. In Argentina, Israel, and Italy.
each of these areas, piXserve
redefines the state of the art and can Corporate
help your meet the efficiency and piXlogic, Inc. T. +1-650-967-4067
4984 El Camino Real E. info@piXlogic.com
effectiveness goals that you have set Suite 205 W. www.piXlogic.com
for yourself. Los Altos, CA 94022
Flex Analytics is a systems integrator and
About piXlogic software reseller in the U.S. Intelligence
piXlogic is a privately held company Community. It supports the sale,
located in Los Altos, CA, USA, the implementation and customization of
heart of Silicon Valley. piXlogic is piXserve in government installations.
an In-Q-Tel portfolio company (a
venture capital organization that Government Sales +1-301-787-2989
Flex Analytics LLC gpepus@flexanalytics.com
serves the needs of the US
10314 Thornbush Ln www.flexanalytics.com
Intelligence Community). The Bethesda, MD 20814
company’s flagship products are
piXserve and piXserve-ALERT.
The software enables:
Content Discovery (find
pictures/videos that contain
specific objects, scenes, text, or
people of interest)
July 2012 pg. 10