Learning Disentangled Representation for Robust Person Re-identificationNAVER Engineering
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. The key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's appearance looks different with viewpoint changes. Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations (e.g., human pose) and this requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to disentangle identity-related and -unrelated features from person images. Identity-related features contain information useful for specifying a particular person (e.g.,clothing), while identity-unrelated ones hold other factors (e.g., human pose, scale changes). To this end, we introduce a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN), that factorizes these features using identification labels without any auxiliary information. We also propose an identity shuffling technique to regularize the disentangled features. Experimental results demonstrate the effectiveness of IS-GAN, largely outperforming the state of the art on standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID. Our code and models will be available online at the time of the publication.
Synthesizing pseudo 2.5 d content from monocular videos for mixed realityNAVER Engineering
Free-viewpoint video (FVV) is a kind of advanced media that provides a more immersive user experience than traditional media. It allows users to interact with content because users can view media at the desired viewpoint and is becoming a next-generation media.
In creating FVV content, existing systems require complex and specialized capturing equipment and has low end-user usability because it needs a lot of expertise to use the system. This becomes an inconvenience for individuals or small organizations who want to create content and limits the end user’s ability to create FVV-based user-generated content (UGC) and inhibits the creation and sharing of various created content.
To tackle these problems, ParaPara is proposed in this work. ParaPara is an end-to-end system that uses a simple yet effective method to generate pseudo-2.5D FVV content from monocular videos, unlike the previously proposed systems. First, the system detects persons from the monocular video through a deep neural network, calculates the real-world homography matrix based on the minimal user interaction, and estimates the pseudo-3D positions of the detected persons. Then, person textures are extracted using general image processing algorithms and placed at the estimated real-world positions. Finally, the pseudo-2.5D content is synthesized from these elements. The content, which is synthesized by the proposed system, is implemented on Microsoft HoloLens; the user can freely place the generated content on the real world and watch it on a free viewpoint.
Learning Disentangled Representation for Robust Person Re-identificationNAVER Engineering
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. The key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's appearance looks different with viewpoint changes. Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations (e.g., human pose) and this requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to disentangle identity-related and -unrelated features from person images. Identity-related features contain information useful for specifying a particular person (e.g.,clothing), while identity-unrelated ones hold other factors (e.g., human pose, scale changes). To this end, we introduce a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN), that factorizes these features using identification labels without any auxiliary information. We also propose an identity shuffling technique to regularize the disentangled features. Experimental results demonstrate the effectiveness of IS-GAN, largely outperforming the state of the art on standard reID benchmarks including the Market-1501, CUHK03 and DukeMTMC-reID. Our code and models will be available online at the time of the publication.
Synthesizing pseudo 2.5 d content from monocular videos for mixed realityNAVER Engineering
Free-viewpoint video (FVV) is a kind of advanced media that provides a more immersive user experience than traditional media. It allows users to interact with content because users can view media at the desired viewpoint and is becoming a next-generation media.
In creating FVV content, existing systems require complex and specialized capturing equipment and has low end-user usability because it needs a lot of expertise to use the system. This becomes an inconvenience for individuals or small organizations who want to create content and limits the end user’s ability to create FVV-based user-generated content (UGC) and inhibits the creation and sharing of various created content.
To tackle these problems, ParaPara is proposed in this work. ParaPara is an end-to-end system that uses a simple yet effective method to generate pseudo-2.5D FVV content from monocular videos, unlike the previously proposed systems. First, the system detects persons from the monocular video through a deep neural network, calculates the real-world homography matrix based on the minimal user interaction, and estimates the pseudo-3D positions of the detected persons. Then, person textures are extracted using general image processing algorithms and placed at the estimated real-world positions. Finally, the pseudo-2.5D content is synthesized from these elements. The content, which is synthesized by the proposed system, is implemented on Microsoft HoloLens; the user can freely place the generated content on the real world and watch it on a free viewpoint.
TVSum: Summarizing Web Videos Using TitlesNEERAJ BAGHEL
Title-based video summarization is a relatively unexplored domain; there is no publicly available dataset suitable for our purpose.
Author therefore collected a new dataset,TVSum50, that contains 50 videos and their shot-level importance scores obtained via crowdsourcing
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
auto-assistance system for visually impaired personshahsamkit73
The World Health Organization (WHO) reported that there are 285 million visually-impaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. One of the most difficult activities that must be conducted by visually impaired is indoor navigation. In indoor environment, visually impaired should be aware of obstacles in front of them and be able to avoid it. The use of powered wheelchairs with high transportability and obstacle avoidance intelligence is one of the great steps towards the integration of physically disabled and mentally handicapped people. The disable person will not be able to visualize the object so this Auto-assistance system may suffice the requirement. Auto-Assistance System operating in dynamic environments need to sense its surrounding environment and adapt the control signal in real time to avoid collisions and protect the users. Auto-Assistance System that assist or replace user control could be developed to serve for these users, utilizing systems and algorithms from Auto-Assistance robots. This system could be used to assist disable in their mobility by warning of obstacles. The system could be used in indoor environment like hospital, public garden area. So, we are designing an Auto-assistance system which will help the visually impaired person to work independently. In this system we would be detecting the obstruction in the path of visually impaired person using USB Camera & help them to avoid the collisions.
GitHub Link: https://github.com/shahsamkit73/Auto-Assistance-System-for-visually-impaired
Александр Заричковый "Faster than real-time face detection"Fwdays
I will talk about object and face detection problems, evolution of different approaches to solving these problems and about the ideas behind each of these approaches. Also I will describe meta-architecture that achieve state of the art results on faces detection problem and works faster than real-time.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar
Atari Game State Representation using Convolutional Neural Networksjohnstamford
I recently gave a talk to some MSc Machine Learning students at De Montfort University about the project I did for my MSc. The work included looking at feature extraction from game screens using the Arcade Learning Environment and Convolutional Neural Networks (CNN).
The work was planned to investigate if the costly nature Q-Learning could be offset by the use of a trained system using 'expert' data. The system uses the same technology as used by Deepmind in their 2013 paper.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
A Framework for Human Action Detection via Extraction of Multimodal FeaturesCSCJournals
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating. 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).
TVSum: Summarizing Web Videos Using TitlesNEERAJ BAGHEL
Title-based video summarization is a relatively unexplored domain; there is no publicly available dataset suitable for our purpose.
Author therefore collected a new dataset,TVSum50, that contains 50 videos and their shot-level importance scores obtained via crowdsourcing
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
auto-assistance system for visually impaired personshahsamkit73
The World Health Organization (WHO) reported that there are 285 million visually-impaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. One of the most difficult activities that must be conducted by visually impaired is indoor navigation. In indoor environment, visually impaired should be aware of obstacles in front of them and be able to avoid it. The use of powered wheelchairs with high transportability and obstacle avoidance intelligence is one of the great steps towards the integration of physically disabled and mentally handicapped people. The disable person will not be able to visualize the object so this Auto-assistance system may suffice the requirement. Auto-Assistance System operating in dynamic environments need to sense its surrounding environment and adapt the control signal in real time to avoid collisions and protect the users. Auto-Assistance System that assist or replace user control could be developed to serve for these users, utilizing systems and algorithms from Auto-Assistance robots. This system could be used to assist disable in their mobility by warning of obstacles. The system could be used in indoor environment like hospital, public garden area. So, we are designing an Auto-assistance system which will help the visually impaired person to work independently. In this system we would be detecting the obstruction in the path of visually impaired person using USB Camera & help them to avoid the collisions.
GitHub Link: https://github.com/shahsamkit73/Auto-Assistance-System-for-visually-impaired
Александр Заричковый "Faster than real-time face detection"Fwdays
I will talk about object and face detection problems, evolution of different approaches to solving these problems and about the ideas behind each of these approaches. Also I will describe meta-architecture that achieve state of the art results on faces detection problem and works faster than real-time.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar
Atari Game State Representation using Convolutional Neural Networksjohnstamford
I recently gave a talk to some MSc Machine Learning students at De Montfort University about the project I did for my MSc. The work included looking at feature extraction from game screens using the Arcade Learning Environment and Convolutional Neural Networks (CNN).
The work was planned to investigate if the costly nature Q-Learning could be offset by the use of a trained system using 'expert' data. The system uses the same technology as used by Deepmind in their 2013 paper.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
A Framework for Human Action Detection via Extraction of Multimodal FeaturesCSCJournals
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating. 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).
Human action recognition with kinect using a joint motion descriptorSoma Boubou
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
Action Genome: Action As Composition of Spatio Temporal Scene GraphsSangmin Woo
Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. Action genome: Actions as composition of spatio-temporal scene graphs. arXiv preprint arXiv:1912.06992, 2019.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Paper presented at the 6th International Work-Conference on Ambient Assisted Living.
Abstract: Due to the increasing demand of multi-camera setup and long-term monitoring in vision applications, real-time multi-view action recognition has gain a great interest in recent years. In this paper, we propose a multiple kernel learning based fusion framework that employs a motion-based person detector for finding regions of interest and local descriptors with bag-of-words quantisation for feature representation. The experimental results on a multi-view action dataset suggest that the proposed framework significantly outperforms simple fusion techniques and state-of-the-art methods.
Development of wearable object detection system & blind stick for visuall...Arkadev Kundu
It is a wearable device. It has a camera, and it detects all living and non living object. This module detects moving object also. It is made with raspberry pi 3, and a camera. One headphone connect with raspberry pi. When this module detects items, it gave a sound output through headphone. Hence the blind man know that item, which is in-front of him or her. We made it in very low budget, and it is very helpful for visually challenged people. And the Blind stick help him to detect obstacles.
Chen Sagiv, co founder and co CEO of SagivTech, gave an introduction talk to Computer Vision at She Codes branch in Google Campus TLV.
In the talk an overview was given on what is computer vision, where it is used, some basic notions and algorithms and the AI revolution.
Silhouette analysis based action recognition via exploiting human posesAVVENIRE TECHNOLOGIES
We propose a novel scheme for human action recognition that combines the advantages of both local and global representations.
We explore human silhouettes for human action representation by taking into account the correlation between sequential poses in an action.
Similar to Sparse representation based human action recognition using an action region-aware dictionary (20)
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...Wesley De Neve
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional neural networks. Paper presented at the Workshop on Computational Biology at the International Conference on Machine Learning, Long Beach, USA, 2019.
Investigating the biological relevance in trained embedding representations o...Wesley De Neve
Investigating the biological relevance in trained embedding representations of protein sequences. Paper presented at the Workshop on Computational Biology at the International Conference on Machine Learning, Long Beach, USA, 2019.
Towards reading genomic data using deep learning-driven NLP techniquesWesley De Neve
Towards reading genomic data using deep learning-driven NLP techniques. Slides presented at BIOINFO 2016 – Precision Bioinformatics & Machine Learning.
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to Smart Farming. Presentation given at the Korea-Europe International Conference on the 4th Industry Revolution.
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...Wesley De Neve
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Target Interaction and DNA Analysis.
Poster presented at the BIG N2N Symposium 2016.
Towards using multimedia technology for biological data processingWesley De Neve
Towards using multimedia technology for biological data processing.
Presentation given during the Ghent University Global Campus (GUGC) Research Seminar on 19/1/2014.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
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We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
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Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
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Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
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Sparse representation based human action recognition using an action region-aware dictionary
1. Sparse Representation-based Human Action Recognition
using an Action Region-aware Dictionary
ISM 2013
December 11, 2013
Hyun-seok Min, Wesley De Neve, and Yong Man Ro
Image and Video Systems Lab
Department of Electrical Engineering
Korea Advanced Institute of Science and Technology (KAIST)
e-mail: hsmin@kaist.ac.kr
web: http://ivylab.kaist.ac.kr
IEEE International Symposium on Multimedia 2013
3. Outline
• Introduction
– human action recognition
– problems
– contributions
• Sparse representation-based human action recognition
• Experiments
• Conclusions and future research
IEEE International Symposium on Multimedia 2013
3
4. Conventional approach for
human action recognition
Pr epr ocessing
Input
Input
Classification
Human Action Recognition Framework
Segmentation
Object Detection
Object Tracking
Video
Sequence
Featur e Ex tr action
Cuboid
SR
2D-Harris
SVM
LBP-TOP
Keypoint
Detection
Output
Random Forest
Descriptor
LMP, CUBOID
IEEE International Symposium on Multimedia 2013
“Skating”
4
5. Action detection vs. action recognition
• A video clip consists of a context region and an action region [1]
– action detection (segmentation) is required for effective action recognition [2]
=
Human action video clip
+
Context region
Action region
• Shortcomings of action detection
– despite the great emphasis on action recognition, there is comparatively little
work available on action detection [2]
– there is currently no general action detection method available that shows a
high level of effectiveness for every action
[1] K K. Reddy and M.Shah, “Recognizing 50 Human Action Categories of Web Videos,” Machine Vision and Applications Journal , vol. 24, no. 5, pp. 971-981, 2012.
[2] S.Sadanand and J.J.Corso, “Action bank: A high-level representation of activity in video,” IEEE Conf. on Computer Vision and Pattern Recognition , pp.1234-1241, 2012.
IEEE International Symposium on Multimedia 2013
5
6. Context information
for human action recognition
• Usefulness of context depends on the action class
(a)
(b)
(c)
– e.g., context is
• helpful for making a distinction between (a) and (b) [3]
• not helpful for making a distinction between (b) and (c)
[3] Tian Lan, Yang Wang, and Greg Mori, “Discriminative Figure-Centric Models for Joint Action Localization and Recognition,” IEEE International Conference on Computer
Vision (ICCV), 2011
IEEE International Symposium on Multimedia 2013
6
7. Research challenges & contributions
• Challenges
– lack of a general method for effective and efficient action detection
– the usefulness of context information depends on the type of action
• Contributions
– we propose a novel human action recognition method
• that does not require complex action detection during testing
• that uses context information in an adaptive way
IEEE International Symposium on Multimedia 2013
7
8. Outline
• Introduction
• Sparse representation-based human action recognition
– conventional method
– proposed method
• construction of an action region-aware dictionary
• use of an action region-aware dictionary
• adaptive classification using split sparse coefficients
• Experiments
• Conclusions and future research
IEEE International Symposium on Multimedia 2013
8
9. Conventional SR-based method:
dictionary construction
…
Action class 1
…
…
Feature
extraction
Action class i
…
…
…
…
Action class K
…
…
…
…
…
i
K
D = [z1 ,..., z1 1 ,..., z1 ,..., ziNi ,...., z1 ,..., z K K ] ∈ ℜ d × N
1
N
N
IEEE International Symposium on Multimedia 2013
9
10. Conventional SR-based method:
classification
•
Input video clip, depicting
'Lifting' (true action)
Given a dictionary D, the feature
vector y of a test video clip V can be
represented as follows
y ≈ Dx∈ ℜ d ,
Sparse coefficients belonging to
the true class
Sparse coefficient value
y : feature vector of V
D : dictionary
x : sparse coefficient vector
•
Given the sparse solution x, we can
calculate the residual error for each
human action as follows:
ri (y) = y − Dδi (x) 1
1
2
3
4
5
Human action class: 1: diving 2: golf swing
6: running 7: skating
6
7
3: kicking
8: swing1
8
4: lifting
9: swing2
9
10
5: riding
10: walking
ri(y) : residual for ith action
δi (x) : a new vector whose only nonzero entries
are the entries in x that are associated
with class i
IEEE International Symposium on Multimedia 2013
10
11. Conventional SR-based method:
dictionary shortcomings
Input video clip, depicting
'Golf' (true action)
• The dictionary only contains
class information
Sparse coefficients belonging to
the true class
Sparse coefficient value
– we do not know the location and
size of the action region of a test
video clip during classification
– however, we do know the
location and size of the action
regions in the training video clips
• Research question
1
2
3
4
5
Human action class: 1: diving 2: golf swing
6: running 7: skating
6
7
3: kicking
8: swing1
8
4: lifting
9: swing2
9
10
5: riding
10: walking
– how about putting the action
region information of the training
video clips in the dictionary?
IEEE International Symposium on Multimedia 2013
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12. Proposed SR-based method:
construction of an action region-aware dictionary
Training video clips
...
...
Segmentation during training
• We propose to construct a
dictionary that consists of
two split dictionaries:
– context region dictionary DC
– action region dictionary DA
Segmented regions
...
...
Action regions
Context regions
Feature extraction
Action region-aware dictionary
D=
...
...
DC
DA
D = [DC | D A ]∈ ℜ d × N
IEEE International Symposium on Multimedia 2013
12
13. Proposed SR-based method:
use of an action region-aware dictionary (1/3)
• Given an action region-aware dictionary D and the feature
vector y of a test video clip V, we can compute the sparse
representation of y as follows
x
y ≈ D R x ≅ [D C | D A ] C = D C xC + D A x A
x A
1
i
K
xC = [ x1,C ,..., x1 1 ,C ,..., x1i,C ,..., xN i ,C ,..., x1KC ,..., xN K ,C ]
,
N
1
i
i
K
x A = [ x1, A ,..., x1 1 , A ,..., x1, A ,..., x Ni , A ,..., x1KA ,..., x N K , A ]
,
N
– xij,C and xij,A: the sparse coefficient values that are associated with the
context and the action region of the jth training video clip
of the ith human action
During testing, the proposed method for human action recognition
is able to automatically make a distinction between information
originating from the context region and information originating from
the action region in a test video clip.
IEEE International Symposium on Multimedia 2013
13
14. Proposed SR-based method:
use of an action region-aware dictionary (2/3)
Input video clip, depicting
'golf swing' (true action)
The sparse coefficients belonging
to the context region of the ‘golf
swing’ test video clip are
dispersed over the different
classes. This can be attributed to
the fact that the background of
‘golf swing’ is visually similar to
the background of ‘kicking’, ‘riding’,
and ‘walking’.
...
Sparse coefficients belonging to
the context region
Sparse coefficient value
Sparse coefficients belonging to
the action region
DC
1
2
3
4
5
6
DA
7
8
9
10
Human action class: 1: diving 2: golf swing
6: running 7: skating
1
2
3
3: kicking
8: swing1
4
5
6
4: lifting
9: swing2
7
8
9
10
5: riding
10: walking
IEEE International Symposium on Multimedia 2013
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15. Proposed SR-based method:
use of an action region-aware dictionary (3/3)
Input video clip, depicting
'diving' (true action)
Sparse coefficients belonging to
the context region
...
The sparse coefficients belonging
to the context region of the
‘diving’ test video clip are
concentrated in the true class.
This means that the context
region of ‘diving’ is different from
the context regions of the other
human actions.
Sparse coefficient value
Sparse coefficients belonging to
the action region
DC
1
2
3
4
5
6
DA
7
8
9
10
Human action class: 1: diving 2: golf swing
6: running 7: skating
1
2
3
3: kicking
8: swing1
4
5
6
4: lifting
9: swing2
7
8
9
10
5: riding
10: walking
IEEE International Symposium on Multimedia 2013
15
16. Adaptive classification using
split sparse coefficients
• Given the above observations, we can hypothesize that
– information originating from context regions can help in successfully classifying
human actions, on the condition that the sparse coefficients associated with the
context regions are concentrated in the true class
• Measurement of the concentration of sparse coefficients
– Maximum Sparse Coefficient Concentration (MSCC)
MSCC (x) = max
k
δk (x) 1
x1
• We can then use the following criterion to determine whether information
of context regions can help in successfully classifying human actions
MSCC (xC )
> ξ ratio
MSCC (x A )
IEEE International Symposium on Multimedia 2013
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17. Outline
• Introduction
• Sparse representation-based human action recognition
• Experiments
– experimental setup
– experimental results
• Conclusions and future research
IEEE International Symposium on Multimedia 2013
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18. Experimental setup (1/2)
• Use of the UCF Sports Action data set
– contains 150 action video clips with a resolution of 720×480, collected
for various sports that are typically featured on broadcast television
channels such as BBC and ESPN
– for each frame, a bounding box is available around the person
performing the action of interest
– available action classes: diving, golf swinging, kicking, lifting, riding,
running, skating, swinging, and walking
Diving
Running
Golf swinging
Kicking
Lifting
Skating
Swinging
Walking
IEEE International Symposium on Multimedia 2013
Riding
18
19. Experimental setup (2/2)
• Comparison with
– SR with action region
• only makes use of action regions in the test video clips considered, thus
taking advantage of segmentation information
– SR with whole region
• uses whole video frames, thus not exploiting segmentation information
SR with whole
region
SR with action
region
IEEE International Symposium on Multimedia 2013
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20. Experimental results (1/2)
• The accuracy of the proposed SR-based method for human action
recognition is more stable over the different human action classes
• The accuracy of the proposed method is highly independent of the
type of human action
– thanks to the use of a context-adaptive classification strategy
IEEE International Symposium on Multimedia 2013
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21. Experimental results (2/2)
• We can observe that what method is most accurate depends on the
human action class considered
– “SR with action region” is usually more accurate when the concentration of
the sparse coefficients associated with the action region is higher than the
concentration of the sparse coefficients associated with the context region
– Otherwise, “SR with whole region” or “Proposed method” are more effective
IEEE International Symposium on Multimedia 2013
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23. Conclusions
• We proposed a novel SR-based method for human action
recognition, having the following two major characteristics
– first, classification does not have to apply explicit segmentation to a
given test video clip
– second, classification is context adaptive in nature, only leveraging
information about the context in which the action took place when
the concentration of the corresponding sparse coefficients is high
IEEE International Symposium on Multimedia 2013
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24. Future research directions
• Use of dictionary learning techniques that allow for more
effective and efficient construction of an overcomplete
dictionary
• Perform experiments with actions that have a lower variation
in background
• Study how to leverage SRC by means of an action regionaware dictionary in other application scenarios
IEEE International Symposium on Multimedia 2013
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25. Thank you!
Any questions?
e-mail: hsmin@kaist.ac.kr .
web: http://ivylab.kaist.ac.kr
IEEE International Symposium on Multimedia 2013
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