The document summarizes research on daily living activity recognition using efficient combination of high and low level cues. The researchers propose an approach that fuses body pose estimation and low-level cues like optical flow to produce an enriched descriptor. A Fisher kernel representation is then used to model the temporal variation in video sequences for recognizing activities. The approach achieves state-of-the-art results on the ADL Rochester dataset.
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This document discusses techniques for detecting anomalies in surveillance videos based on saliency detection and segmentation. It proposes extracting salient objects from motion fields using saliency detection algorithms. Surveillance videos capture behavioral activities, with some frequent sequences considered normal and deviations considered anomalies that could indicate criminal activity. The document describes calculating image gradients, thresholding, using a Sobel edge detector, and implementing the proposed system to detect anomalies by recognizing actions, detecting objects, and identifying moving regions in test video frames. Experimental results on test videos demonstrate action recognition, object detection, and identification of anomalies.
This document provides an overview of image features and categorization in computer vision. It discusses why categorization is important for making predictions about objects and communicating categories. It describes approaches to categorization like definitional, prototype, and exemplar models. Common image features for categorization like color, texture, gradients, and interest points are presented. Methods for representing images as histograms of these features and encoding local descriptors as "bags of visual words" are covered. Deep convolutional neural networks and region-based representations are also summarized. The document aims to explain current techniques for image and region categorization using supervised learning of classifiers on labeled examples and extracted image features.
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.
Sparse representation based human action recognition using an action region-a...Wesley De Neve
This document presents a paper on sparse representation-based human action recognition using an action region-aware dictionary. It introduces the challenges of existing action recognition methods, including the lack of a general action detection method and the varying usefulness of context information depending on the action. The paper proposes constructing a dictionary containing separate context and action region information from training videos. It then presents a method to use this dictionary to adaptively classify human actions based on whether context region information is concentrated in the true class. The paper describes experiments on the UCF Sports Action dataset to evaluate the proposed method compared to existing sparse representation approaches.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
This document discusses deep learning techniques for person re-identification. It begins with an overview of supervised and unsupervised person re-identification. It then discusses the challenges of annotation cost and data size for re-ID. Next, it covers active learning approaches for person re-ID using human-in-the-loop feedback to incrementally train models. Finally, it discusses relationships between person re-ID and attribute learning, person detection, and multi-target multi-camera tracking.
Virtual/ Physical Co-Existing Design (CapX)Kai-Tzu Lu
CapX: Co-existing Design Capturing Device is about establishment an interface for coexistence of hardware and software. Through Director lingo a 3D virtual stage is established. External files are actively transmitted into the 3D virtual stage. Action script is connected with electronic hardware device Teleo Board to transmit computer-imaged interface and interactive virtual signals into the physical device and change the state of the physical space.
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This document discusses techniques for detecting anomalies in surveillance videos based on saliency detection and segmentation. It proposes extracting salient objects from motion fields using saliency detection algorithms. Surveillance videos capture behavioral activities, with some frequent sequences considered normal and deviations considered anomalies that could indicate criminal activity. The document describes calculating image gradients, thresholding, using a Sobel edge detector, and implementing the proposed system to detect anomalies by recognizing actions, detecting objects, and identifying moving regions in test video frames. Experimental results on test videos demonstrate action recognition, object detection, and identification of anomalies.
This document provides an overview of image features and categorization in computer vision. It discusses why categorization is important for making predictions about objects and communicating categories. It describes approaches to categorization like definitional, prototype, and exemplar models. Common image features for categorization like color, texture, gradients, and interest points are presented. Methods for representing images as histograms of these features and encoding local descriptors as "bags of visual words" are covered. Deep convolutional neural networks and region-based representations are also summarized. The document aims to explain current techniques for image and region categorization using supervised learning of classifiers on labeled examples and extracted image features.
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.
Sparse representation based human action recognition using an action region-a...Wesley De Neve
This document presents a paper on sparse representation-based human action recognition using an action region-aware dictionary. It introduces the challenges of existing action recognition methods, including the lack of a general action detection method and the varying usefulness of context information depending on the action. The paper proposes constructing a dictionary containing separate context and action region information from training videos. It then presents a method to use this dictionary to adaptively classify human actions based on whether context region information is concentrated in the true class. The paper describes experiments on the UCF Sports Action dataset to evaluate the proposed method compared to existing sparse representation approaches.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
This document discusses deep learning techniques for person re-identification. It begins with an overview of supervised and unsupervised person re-identification. It then discusses the challenges of annotation cost and data size for re-ID. Next, it covers active learning approaches for person re-ID using human-in-the-loop feedback to incrementally train models. Finally, it discusses relationships between person re-ID and attribute learning, person detection, and multi-target multi-camera tracking.
Virtual/ Physical Co-Existing Design (CapX)Kai-Tzu Lu
CapX: Co-existing Design Capturing Device is about establishment an interface for coexistence of hardware and software. Through Director lingo a 3D virtual stage is established. External files are actively transmitted into the 3D virtual stage. Action script is connected with electronic hardware device Teleo Board to transmit computer-imaged interface and interactive virtual signals into the physical device and change the state of the physical space.
This document contains a list of 40 MATLAB project titles from 2013 organized by project code and title. The projects focus on topics related to image processing, computer vision, and machine learning including image segmentation, saliency detection, denoising, tracking, and enhancement. The document also provides contact information for S3 Infotech.
The document discusses magazine design choices such as:
1) Placing the masthead in the top left corner to make it eye-catching while allowing space for other elements.
2) Having the main cover line in the bottom left corner to allow for various images on the front page.
3) Incorporating a large, stylish central image on the cover to draw attention instead of multiple smaller photos.
This document summarizes the key conventions used in music magazine design. It discusses elements like the title/logo using sans serif fonts and taking up the full width. The background uses bright colors to stand out on shelves. Images are included to show what will be featured in articles. Cover lines provide insights into inside content. The lead article is the main cover story to draw readers in. Contents pages list article locations. Editors' notes introduce each issue.
Social media has changed the way journalists reach their audience. Google Plus is the next rising social media website for journalists to connect to their audience. Watch this presentation for a crash course in Google Plus and the benefits available to you as a journalist.
El documento describe los productos y platos típicos de la provincia de Loja en Ecuador. Explica que Loja se destaca por la producción de granos, cereales y ganado vacuno que se usan para preparar platos como el tamal lojano, ensalada de frejol con papas, y sango lojano. También presenta recetas detalladas de cinco platos populares de la región: ensalada de frejol con papas, ensalada de mellocos, sango lojano, aji de carne y sopa de avena tost
A Modified Feature Relevance Estimation Approach to Relevance Feedback in Con...Ionut Mironica
This document presents a relevance feedback approach to video genre retrieval. It discusses using relevance feedback algorithms like Rocchio, feature relevance estimation, SVM, and hierarchical clustering relevance feedback to improve content-based video retrieval systems. An evaluation of these algorithms on a test database of over 90 hours of video across 7 genres showed that the hierarchical clustering relevance feedback approach outperformed other methods, achieving a mean precision improvement of 76.61% compared to 40.82% for the initial descriptor alone. Future work could involve testing the approach on larger, more challenging databases.
This document provides 5 practical tips for managing change: 1) Start with defining success criteria and timeline. 2) Communicate the benefits of change to stakeholders and address concerns. 3) Discuss options to alleviate fears and help achieve early wins. 4) Identify supporters to promote the vision. 5) Maintain positivity while respecting different perspectives and focusing on possibilities. The overall message is that successful change requires effective leadership through communication, addressing resistance, and focusing on benefits.
This document provides 7 time management tips for busy teachers:
1. Prioritize tasks by importance and urgency, completing important urgent tasks first.
2. Factor in potential problems and delays by building in buffers and asking others questions upfront.
3. Schedule mentally taxing tasks for when you have the most energy, such as in the morning.
4. Set clear expectations and instructions when delegating tasks to others.
5. Be assertive when others ask for your time and have them summarize their request.
6. Multi-task to stay productive by having several tasks in progress at once.
7. Tackle unpleasant or difficult tasks first thing in the morning to feel a sense of accomplishment
The document provides an analysis of the language used in newspaper articles about riots in London in 2011. It finds that the articles consistently portray the young people involved in a negative light through the use of biased and emotive language. They are referred to as "thugs", "feral kids", and emphasis is placed on their hoodies and masks to imply criminal intent. No reasons for the riots are explored and the perspectives of the young people are not included. Binary oppositions are created between the young rioters and normal, law-abiding citizens. The analysis concludes that the newspaper coverage exaggerated the dangers of the riots and used powerful language to create a strong negative representation of the young people as wild, out of control, and rebelling
An In-Depth Evaluation of Multimodal Video Genre CategorizationIonut Mironica
The document describes research on multimodal video genre categorization. It evaluates different techniques for fusing audio, visual, and text features to classify videos into genres. The best individual modality performances were 42.33% for audio features using Extremely Random Forests and 26.17% for MPEG-7 visual features also using Extremely Random Forests. Fusion of modalities improved classification accuracy over the individual modalities.
This document proposes a novel framework for adaptively learning a partial differential equation (PDE) system from an image for visual saliency detection. It assumes saliency of image elements can be carried out from their relevance to saliency seeds. It introduces a general linear elliptic system with Dirichlet boundary conditions to model diffusion from seeds to other relevant points. For a given image, it first learns a guidance map fusing human priors, then optimizes the saliency seeds, achieving an optimal PDE system to model visual saliency evolution. Quantitative results on standard datasets show the approach achieves state-of-the-art saliency detection performance.
This document provides a summary of Carissa Davis's involvement and experience at UNC Chapel Hill. It lists that she is a junior and involved in several campus organizations focused on diversity and multicultural affairs, including the Office of Diversity and Multicultural Affairs, the Minority Student Recruitment Committee, Omega Phi Beta Sorority, and the Carolina Hispanic Association. It also notes she is involved with the Que Rico Latin Dance Team.
This document provides a description of an ideal future school. The school is called the "Extraordinary School" and is painted blue and yellow-green. It features a glass domed room with an indoor garden and exotic plants. Classrooms are decorated according to their subject matter, such as aquariums between the natural history room and corridor. Students can observe plant growth and participate in meetings under the dome. Astronomy lessons make use of projections on the dome's interior surface. Students and teachers have a respectful relationship and dress casually in calm colors. Learning occurs five days a week with weekends for rest and recharging for the following week.
The document discusses how a music video project uses, develops, and challenges conventions of indie rock music videos. It summarizes that:
1) Shots establishing narrative scenes through shot reverse shot and two shots follow conventions. Performance and narrative scenes mixed also follow conventions.
2) Using many locations (12 in this video) to maintain audience interest follows conventions seen in other indie rock music videos.
3) Linking lyrics visually, like writing a list when the lyrics say "I bet you've got a list," develops the illustrative connection between words and images.
4) Elements like costumes, lighting, camera angles, and shot types are used to follow genre conventions while also attempting some technical challenges like
Saliency Detection via Divergence Analysis: A Unified Perspective ICPR 2012Jia-Bin Huang
A number of bottom-up saliency detection algorithms have been proposed in the literature. Since these have been developed from intuition and principles inspired by psychophysical studies of human vision, the theoretical relations among them are unclear. In this paper, we present a unifying perspective. Saliency of an image area is defined in terms of divergence between certain feature distributions estimated from the
central part and its surround. We show that various, seemingly different saliency estimation algorithms are in fact closely related. We also discuss some commonly
used center-surround selection strategies. Experiments with two datasets are presented to quantify the relative advantages of these algorithms.
Best student paper award in Computer Vision and Robotics Track
Human Action Recognition Based on Spacio-temporal featuresnikhilus85
This document summarizes a technique for human action recognition based on spatio-temporal features. The technique uses Lucas-Kanade optical flow to extract motion features and Viola-Jones features to extract shape features from localized regions of interest in video frames. These motion and shape features are combined over a period of time to form spatio-temporal features, which are then classified using AdaBoost to recognize different human actions. The technique is applied to the Weizman human action dataset and achieves accurate action recognition results.
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
This document contains a list of 40 MATLAB project titles from 2013 organized by project code and title. The projects focus on topics related to image processing, computer vision, and machine learning including image segmentation, saliency detection, denoising, tracking, and enhancement. The document also provides contact information for S3 Infotech.
The document discusses magazine design choices such as:
1) Placing the masthead in the top left corner to make it eye-catching while allowing space for other elements.
2) Having the main cover line in the bottom left corner to allow for various images on the front page.
3) Incorporating a large, stylish central image on the cover to draw attention instead of multiple smaller photos.
This document summarizes the key conventions used in music magazine design. It discusses elements like the title/logo using sans serif fonts and taking up the full width. The background uses bright colors to stand out on shelves. Images are included to show what will be featured in articles. Cover lines provide insights into inside content. The lead article is the main cover story to draw readers in. Contents pages list article locations. Editors' notes introduce each issue.
Social media has changed the way journalists reach their audience. Google Plus is the next rising social media website for journalists to connect to their audience. Watch this presentation for a crash course in Google Plus and the benefits available to you as a journalist.
El documento describe los productos y platos típicos de la provincia de Loja en Ecuador. Explica que Loja se destaca por la producción de granos, cereales y ganado vacuno que se usan para preparar platos como el tamal lojano, ensalada de frejol con papas, y sango lojano. También presenta recetas detalladas de cinco platos populares de la región: ensalada de frejol con papas, ensalada de mellocos, sango lojano, aji de carne y sopa de avena tost
A Modified Feature Relevance Estimation Approach to Relevance Feedback in Con...Ionut Mironica
This document presents a relevance feedback approach to video genre retrieval. It discusses using relevance feedback algorithms like Rocchio, feature relevance estimation, SVM, and hierarchical clustering relevance feedback to improve content-based video retrieval systems. An evaluation of these algorithms on a test database of over 90 hours of video across 7 genres showed that the hierarchical clustering relevance feedback approach outperformed other methods, achieving a mean precision improvement of 76.61% compared to 40.82% for the initial descriptor alone. Future work could involve testing the approach on larger, more challenging databases.
This document provides 5 practical tips for managing change: 1) Start with defining success criteria and timeline. 2) Communicate the benefits of change to stakeholders and address concerns. 3) Discuss options to alleviate fears and help achieve early wins. 4) Identify supporters to promote the vision. 5) Maintain positivity while respecting different perspectives and focusing on possibilities. The overall message is that successful change requires effective leadership through communication, addressing resistance, and focusing on benefits.
This document provides 7 time management tips for busy teachers:
1. Prioritize tasks by importance and urgency, completing important urgent tasks first.
2. Factor in potential problems and delays by building in buffers and asking others questions upfront.
3. Schedule mentally taxing tasks for when you have the most energy, such as in the morning.
4. Set clear expectations and instructions when delegating tasks to others.
5. Be assertive when others ask for your time and have them summarize their request.
6. Multi-task to stay productive by having several tasks in progress at once.
7. Tackle unpleasant or difficult tasks first thing in the morning to feel a sense of accomplishment
The document provides an analysis of the language used in newspaper articles about riots in London in 2011. It finds that the articles consistently portray the young people involved in a negative light through the use of biased and emotive language. They are referred to as "thugs", "feral kids", and emphasis is placed on their hoodies and masks to imply criminal intent. No reasons for the riots are explored and the perspectives of the young people are not included. Binary oppositions are created between the young rioters and normal, law-abiding citizens. The analysis concludes that the newspaper coverage exaggerated the dangers of the riots and used powerful language to create a strong negative representation of the young people as wild, out of control, and rebelling
An In-Depth Evaluation of Multimodal Video Genre CategorizationIonut Mironica
The document describes research on multimodal video genre categorization. It evaluates different techniques for fusing audio, visual, and text features to classify videos into genres. The best individual modality performances were 42.33% for audio features using Extremely Random Forests and 26.17% for MPEG-7 visual features also using Extremely Random Forests. Fusion of modalities improved classification accuracy over the individual modalities.
This document proposes a novel framework for adaptively learning a partial differential equation (PDE) system from an image for visual saliency detection. It assumes saliency of image elements can be carried out from their relevance to saliency seeds. It introduces a general linear elliptic system with Dirichlet boundary conditions to model diffusion from seeds to other relevant points. For a given image, it first learns a guidance map fusing human priors, then optimizes the saliency seeds, achieving an optimal PDE system to model visual saliency evolution. Quantitative results on standard datasets show the approach achieves state-of-the-art saliency detection performance.
This document provides a summary of Carissa Davis's involvement and experience at UNC Chapel Hill. It lists that she is a junior and involved in several campus organizations focused on diversity and multicultural affairs, including the Office of Diversity and Multicultural Affairs, the Minority Student Recruitment Committee, Omega Phi Beta Sorority, and the Carolina Hispanic Association. It also notes she is involved with the Que Rico Latin Dance Team.
This document provides a description of an ideal future school. The school is called the "Extraordinary School" and is painted blue and yellow-green. It features a glass domed room with an indoor garden and exotic plants. Classrooms are decorated according to their subject matter, such as aquariums between the natural history room and corridor. Students can observe plant growth and participate in meetings under the dome. Astronomy lessons make use of projections on the dome's interior surface. Students and teachers have a respectful relationship and dress casually in calm colors. Learning occurs five days a week with weekends for rest and recharging for the following week.
The document discusses how a music video project uses, develops, and challenges conventions of indie rock music videos. It summarizes that:
1) Shots establishing narrative scenes through shot reverse shot and two shots follow conventions. Performance and narrative scenes mixed also follow conventions.
2) Using many locations (12 in this video) to maintain audience interest follows conventions seen in other indie rock music videos.
3) Linking lyrics visually, like writing a list when the lyrics say "I bet you've got a list," develops the illustrative connection between words and images.
4) Elements like costumes, lighting, camera angles, and shot types are used to follow genre conventions while also attempting some technical challenges like
Saliency Detection via Divergence Analysis: A Unified Perspective ICPR 2012Jia-Bin Huang
A number of bottom-up saliency detection algorithms have been proposed in the literature. Since these have been developed from intuition and principles inspired by psychophysical studies of human vision, the theoretical relations among them are unclear. In this paper, we present a unifying perspective. Saliency of an image area is defined in terms of divergence between certain feature distributions estimated from the
central part and its surround. We show that various, seemingly different saliency estimation algorithms are in fact closely related. We also discuss some commonly
used center-surround selection strategies. Experiments with two datasets are presented to quantify the relative advantages of these algorithms.
Best student paper award in Computer Vision and Robotics Track
Human Action Recognition Based on Spacio-temporal featuresnikhilus85
This document summarizes a technique for human action recognition based on spatio-temporal features. The technique uses Lucas-Kanade optical flow to extract motion features and Viola-Jones features to extract shape features from localized regions of interest in video frames. These motion and shape features are combined over a period of time to form spatio-temporal features, which are then classified using AdaBoost to recognize different human actions. The technique is applied to the Weizman human action dataset and achieves accurate action recognition results.
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
HUMAN ACTION RECOGNITION IN VIDEOS USING STABLE FEATURES sipij
Human action recognition is still a challenging problem and researchers are focusing to investigate this
problem using different techniques. We propose a robust approach for human action recognition. This is
achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP)
and scale invariant feature transform (SIFT). These features are used to train an MLP neural network
during the training stage, and the action classes are inferred from the test videos during the testing stage.
The proposed features well match the motion of individuals and their consistency, and accuracy is higher
using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly
used for human action recognition. In addition, we show that our approach outperforms individual features
i.e. considering only spatial and only temporal feature.
This slide provides the overview of object detection, real time challenging issue and application.
This slide also provides some implemented work outcomes.
This document summarizes the skills and experience of Yanjun Chen, including 6 years of experience in optical, electrical, and mechanical subsystem design, system integration, and image processing. Chen has a M.S. in Bioengineering from UIC and B.S. in Optoelectronic Information Engineering from Harbin Institute of Technology. Areas of expertise include optical coherence tomography, adaptive optics, and ultra-precision motion control.
This document summarizes recent advances in human pose estimation using deep learning methods. It first discusses traditional approaches like pictorial structures. It then covers several deep learning methods including global/holistic view using joint regression, local appearance using body part detection, and combining global and local information. Other methods discussed are using motion features and pose estimation in videos. Evaluation metrics like PCP and PDJ are also introduced. The document outlines many key papers in this area and provides examples of network architectures and results.
IRJET- Identification of Missing Person in the Crowd using Pretrained Neu...IRJET Journal
The document describes a proposed system to identify missing persons in crowded areas using pretrained convolutional neural networks. The system would involve collecting images of missing persons from different angles to create a dataset for training. An AlexNet pretrained neural network would then be used to detect faces in live video captured by a drone camera of crowded areas. Detected faces would be cropped, stored in a database, and used to further train the network. During testing, the system could identify missing persons by displaying their images when detected in the crowd. The goal of the system is to help police efficiently locate missing people in crowded public settings like festivals or meetings.
Here is my updated CV using the ModernCV template (http://www.latextemplates.com/template/moderncv-cv-and-cover-letter).
You can find the Tex source file in (https://dl.dropbox.com/u/2810224/Homepage/resume/modern%20style.rar)
The document summarizes a research paper titled "HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences". It proposes a novel descriptor called HON4D that encodes the distribution of surface normal orientations in a 4D space of depth, time, and spatial coordinates for activity recognition from depth image sequences. The 4D space is quantized using the vertices of a polychoron structure to create bins. This allows the HON4D descriptor to capture more complex and articulated motions than existing holistic approaches. Evaluation shows it outperforms these prior methods and can also be adapted for unaligned dataset recognition.
Predicting Media Memorability with Audio, Video, and Text representationsAlison Reboud
This study evaluated different modalities - audio, video, and text representations - for predicting media memorability over short and long terms. The results showed that combining modalities achieved the best performance, with a Spearman score of 0.101 for short term and 0.078 for long term memorability prediction. While individual modalities like audio-visual and text features had lower scores, multimodal fusion outperformed individual modalities. Additionally, using short term memorability predictions led to better results for long term memorability prediction compared to models directly predicting long term memorability.
Unsupervised object-level video summarization with online motion auto-encoderNEERAJ BAGHEL
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day.
Author investigate a pioneer research direction towards the unsupervised object-level video summarization.
It can be distinguished from existing pipelines in two aspects:
Extracting key motions of participated objects
Learning to summarize in an unsupervised and online manner.
Technical Evaluation of HoloLens for Multimedia: A First LookHaiwei Dong
- The document reports on a study that evaluated the performance of Microsoft HoloLens across five key areas: head localization, real environment reconstruction, spatial mapping, hologram visualization, and speech recognition.
- Experiments were conducted with 20 participants and involved comparing HoloLens outputs to a ground truth motion capture system.
- The results showed that HoloLens estimated head posture most accurately at low movement speeds, reconstructed environments most precisely for flat surfaces in bright conditions, anchored augmented content most accurately at distances of 1.5-2.5m, displayed holograms with an average size error of 6.64%, and recognized speech commands correctly 74.47% and 66.87% of the time for user-defined
The document describes a method for using SURF (Speeded Up Robust Features) to detect objects and help visually impaired people locate personal items. SURF is used to extract distinctive features from images of objects. When a user requests to find an item, SURF features are matched between the query image and a database of item images. If a match is found, an audio signal identifies the location of the item to the user. The method was tested on images with different orientations and scales. SURF performed well on normally oriented images but less well on rotated images, though it was faster than other methods. The goal is to develop an effective and efficient algorithm to assist visually impaired users in locating personal items through audio
This document provides an overview of recent developments in sound recognition techniques. It discusses several methods for sound recognition, including matching pursuit algorithms with MFCC features, probabilistic distance support vector machines using generalized gamma modeling of STE features, and frequency vector principal component analysis. The document also reviews related literature on environmental sound recognition using time-frequency audio features and sound event recognition. It aims to present an updated survey on sound recognition methods and discuss future research trends in the field.
This document discusses motion segmentation techniques. It begins by defining motion segmentation as segmenting images based on common motion, where points moving together are grouped together. It then lists some applications of motion segmentation such as object detection, tracking, and video editing. The document goes on to discuss previous work on motion segmentation algorithms. It also outlines some challenges in motion segmentation like computing motion in complex scenes with many objects. Finally, it provides an overview of topics covered in more detail, such as feature tracking, motion segmentation using mixture models, and articulated human motion models.
This document presents an audio-visual emotion recognition system that uses multiple modalities and machine learning techniques. It extracts audio features like MFCCs and visual features like facial landmarks from video clips. It uses classifiers like CNNs and stacks their confidence outputs to predict emotions. The system achieves state-of-the-art performance on several databases according to experiments. It represents an improvement over previous work by combining audio, visual and classifier fusion approaches for multimodal emotion recognition.
YOLO BASED SHIP IMAGE DETECTION AND CLASSIFICATIONIRJET Journal
This document presents a method for ship image detection and classification using YOLO and CNNs. It proposes using a CNN to extract features from input ship images, which are then fed into an SVM classifier to improve classification performance over a standard CNN. The method achieved 98% accuracy. It discusses applying deep learning techniques like CNNs to overcome limitations of traditional machine learning for complex computer vision tasks using image data. The document also provides background on deep learning, CNNs, neural networks, and challenges in ship detection from remote sensing images.
IRJET- A Review on Moving Object Detection in Video Forensics IRJET Journal
This document reviews research on moving object detection in video forensics. It discusses challenges in analyzing large amounts of surveillance video data and summarizes several papers that propose methods for tasks like video synopsis, abandoned object detection, person identification, copy-move forgery detection, and assessing evidence quality. The goal is to develop techniques for efficiently analyzing video evidence and detecting anomalies or tampering.
Selective local binary pattern with convolutional neural network for facial ...IJECEIAES
Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.
Selective local binary pattern with convolutional neural network for facial ...
Iciap 2
1. Daily Living Activities Recognition via Efficient
High and Low Level Cues Combination and
Fisher Kernel Representation
Negar Rostamzadeh1
Gloria Zen1
Ionut Mironica2
Jasper Uijlings1
Nicu Sebe1
1 DISI, University of Trento, Trento, Italy
2 LAPI, University Politehnica of Bucharest, Bucharest, Romania
3. Action Recognition in videos
Answer phone or dial phone?
Difficulties in fine-grained activities:
1. Slightly different activities in motion and appearance
2. Different manner of performing the similar task.
Motivation – State of the art – Our approach – Results - Conclusion 3/23
4. Object Centric approaches- SoA
Object-centric approaches- based on tracking and
trajectory analysis6,16
Advantages
Brendel et al, ICCV 2011 [5], May et al, CVPR 2004 [6]
Campos et al, WACV 2011 [23], Liu et al, CVPR 2007[16]
Limitations
Providing semantic/high-level
information of the scene
Handling occlusions in objects interactions
The broken and missed trajectories
The problem of curse of dimensionality
Motivation – State of the art – Our approach – Results - Conclusion 4/23
5. Non-object centric approaches - SoA
Bag-of-words approach relying on low-level
HoG STIP Foreground pixels HoF
Advantages
Laptev et al, CVPR 2008 [1], Willems et al, ECCV 2008 [2], Hospedales et al, ICCV 2009. Zen et al, CVPR 2011 [4],
Wong et al, CVPR 2007 [15], Chang et al, ICCV 2011 [17], Gilbert et al, ICCV 2009 [19],
Zelniker et al, ICCV 2008 [20], Gehrig et al, ICCV 2009[21], Mahbub et al, ICIEV [25]
5/23
Limitations
Robustness to noise & occlusions
Computational efficiency
1. Discard semantic & high-level
information of the scene.
2. Discard relationship among spatio-temporal
local features.
Motivation – State of the art – Our approach – Results - Conclusion
6. Enhanced descriptors - SoA
1. Relation between local features
Pair-wise10,11,12,18, local space or time neighborhood11,18, ST phrases9
2. Combining different local features
Such as local motion, appearance, and positions14,24
3. Enriching the combination of low level features with high- level
information
Detect and localize faces7, STIP volume8,9
Which body-part causes what motion?
Messing et al, ICCV, 2009 [7], Fathi et al, 2008 [8], Zhang et al, 2012 [9], Matikainen et al, 2010 [10], : Gaur et al, 2011 [11],
Savarese et al, 2008 [12], Kovashka et al, Gireddy et al, 2011 [14], CVPR 2007[18] , Shechtman et al, CVPR 2011 [24]
6/23
Motivation – State of the art – Our approach – Results - Conclusion
7. Fusing information
to produce enriched
Low-level cues
Input Video Classifier
descriptor
Apply a Feature-representation
Recognizing
Activities
Body-part
detector
Accumulation over
each video
Fisher Kernel to
model the Temporal
variation
Approach in a glance
Motivation – State of the art – Our approach – Results - Conclusion 7/25
8. Enhanced pose estimator
Body-pose estimation
What is the problem with an off-the-shelf detector?
Our Solution:
Employ an already-trained off-the-shelf
detector
We use the already trained
classifier, but we provide some
additional information from the
new dataset
BUFFY ADL
Motivation – State of the art – Our approach – Results - Conclusion 8/25
9. Enhanced pose estimator
Body-pose estimation- build on ‘Yang and Ramanan PAMI2012, CVPR11’ [29]
9/25
1. Model the body as a pictorial structure (Felzenshwalb-CVPR 2010)
2. Model the body as a Tree
3. Each possible body-configuration has a score
Local score: HoG Pair-wise score
HoG -
appearance
Scores by employing off-the-shelf
detector = Sinitial
Motivation – State of the art – Our approach – Results - Conclusion
10. 10/25
Enhanced pose estimator
Relative importance of foreground
and optical flow score
New Score = Sinitial
weights
Foreground Score Optical Flow Score
Motivation – State of the art – Our approach – Results - Conclusion
11. Enhanced pose estimator
New Score = Sinitial
SSooAA OOuurr a apppprrooaacchh OFpotriceaglr Foluonwd
SoA Our approach Optical Flow SoA Our approach Foreground
Motivation – State of the art – Our approach – Results - Conclusion 11/25
12. 12/25
Enhanced pose estimator used to enrich action
recognition approach
New Score = Sinitial
Tuning
Motivation – State of the art – Our approach – Results - Conclusion
13. Body-part
detector
Input Video
Fusing information
to produce enriched
descriptor
Low-level cues
Accumulation over
each video
Classifier
Apply a Feature-representation
Recognizing
Activities
Fisher Kernel to
model the Temporal
variation
Approach in a glance
Motivation – State of the art – Our approach – Results - Conclusion 13/25
14. Fisher Kernel (FK) Theory
Fisher Kernel in the state-of-the-art
1. Introduced by Jaakkol NIPS’99 [26]) for protein detection
2. Web audio classification (Moreno 2000)
3. Introduced in Computer Vision for Image categorization by [Perronnin, CVPR’07]
Fisher Kernel in image categorization Vs video analysis
1. Modeling the : spatial variation temporal variation
2. Visual documents: small patches frames of the video
3. Initial feature vectors : SIFT our novel descriptors for
action recognition
Motivation – State of the art – Our approach – Results - Conclusion 14/25
15. Fisher Kernel (FK) Theory
- combines the benefits of generative and discriminative approaches
- represents a signal as the gradient of the probability density function that is a learned
generative model of that signall
Motivation – State of the art – Our approach – Results - Conclusion 15/25
16. Results on the ADL Rochester dataset
Motivation – State of the art – Our approach – Results - Conclusion 16/25
17. Conclusion
17/25
We proposed a novel descriptor that is
combining high-level semantic information and
low–level cues.
We propose an enhanced body-pose estimator.
We model the Temporal variation by the Fisher-
Kernel representation.
Motivation – State of the art – Our approach – Results - Conclusion
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Editor's Notes
Hello, my name is Negar Rostamzadeh. I am working under the supervision of prof. Nicu Sebe at the University of Trernto, Italy. This work is a joint work with my colleagues Gloria Zen, Ionut Mironica and Jasper Ujilings.
To start with, I will talk about the difficulty of action recognition in daily living scenarios.
I will first cover the related work and then I will present the steps of the approach & the action recognition and pose estimation results on ADL dataset. Finally, I will draw some concluding remarks.
Human centric action recognition is always a challenging problem both in images and in videos. In some video scenarios, it is possible to extract a few frames from the video and then label the activity based on the information taken from those single frames, while in some scenarios it is very difficult to label the activities based on just a few frames. As an example take a look at these 2 images. In both there is a person who is looking at a cell phone but without looking at the next frames whether he is going to answer the phone or dial a phone number, we don’t have enough cues to recognize among these 2 actions.
As you see fine grained activities are the activities that are slightly different with each other in terms of motion, appearance and the objects that are present in the scene. Moreover the same action can also be done in very different manners.
Now let’s see what has been done in the state-of-the-art. There is a type of approach in the state-of-the-art that can be called Object-centric approaches. These approaches are based on defining objects of interests (here let’s say human), detecting and tracking them in consecutive frames. These approaches provide high-level information about the detected objects but they are unable to handle occlusions and the broken trajectories problem may occur.
The other group of approaches are non-object-centric approaches. Non-object centric approaches are popular nowadays and they are based on low level cues in a bag-of-words framework. These approaches are more robust to noise and occlusions and computationally more efficient but they also have some limitation. 1st of all because of jumping directly from low-level cues to the high-level information some semantic and high-level information is discarded. Moreover the relationship among low-level cues is discarded while these cues are analyzed in a bag-of-words framework.
Well, some approaches addressed these problems and tried to solve them by enhancing the descriptors. Some of them involve the relationship among local features in the descriptor. Such as the relation among pairs of local features, relation in the time or space local neighborhood and recently the space-time phrases. Some tried to enrich descriptors with a combination of different local features, such as local motions, local appearance and positions. Finally the 3d types of method enrich descriptors by a combination of local-features and high-level information that comes from detectors. Our approach can also be considered in this group. We define different parts of the body as our semantic classes and try to detect them and find out which motion is belonging to which body-part? Which body-part causes what motion?
In this work we enrich the descriptor by a combination of low and high level information. Local motion is obtained by optical flow and quantized in 8 directions. Detected body parts represent different semantic classes. Then for fusing this information we make a vector of 8 bins for each body part and find the motions that are belonging to each body part. Afterward we concatenate the motion vectors of all semantic classes. Then we represent feature descriptors of different videos by 2 representations. The first one is a simple accumulation of these feature vectors over each video and the second one is with the use of fisher kernel representation in order to model the temporal variation. Then by applying a classification method these videos are classified and the recognizing task is finished. In the following section I will also talk about our enhanced body-part detector method and applying the fisher kernel approach to model the temporal variation.
In the case of body pose estimation, a significant drop in accuracy has been observed when
a detector is trained on one dataset and it is evaluated on a different one. A possible solution to this is to set the body pose groundtruth for the new
dataset and re-train the classifier. However this procedure is very expensive and requires a consistent delay every time a new dataset has to be analyzed. Instead of training another classifier on the new dataset, we propose to use the already trained classifier, but we provide some additional information from the new dataset.
We build our approach on top of “Yang and Ramanan” approach that is among the best approaches in the literature. I will firstly summarize their approach.
Their model is a pictorial model. In the pictorial structure body is modeled as an ideal template in a graph structure, while single body parts are the nodes of graph and edges presented by springs. Then different configuration are made by deforming the main template.
Yang and Ramanan model this graph as a tree to simplify the model and also each node just be connected with a spring to a parent node.
Then a score is given to each possible body-configuration.
This score is made by summing up the single body part scores and pairwise scores. We call this score as the initial score.
Here I have presented our new score. These are the foreground and optical flow scores. FG score represent the ratio number of foreground pixels presented in a box inside the predicted bounding box. The size of this box is related to the parameter gamma. The OF score is also computed similarly as the ratio number of ofs in a box with the related size of lambda. Beta and eta represent the weights of fg and of scores, while alpha is the relative importance of these 2 score. It means that alpha present which of the optical flow or foreground score improves the detection rate more.
Here I have presented the case that alpha is equal to 0. it means just the foreground is considered. This fig represent avg body-pose estimation accuracy for different optical flow box size and its weight. Here a sample is presented in which the left hand is not detected well while by involving the of information and increasing the score of the parts that are moving the left hand correctly is detected in our approach. Similarly here I presented the results for considering different beta and gamma and in the figures you see the right hand is mistakenly detected, while by involving foreground information it has correctly detected.
This figure also presents the relative importance of fg and of scores and its affect on the accuracy. As we presented in the paper oF increases the detection rate of the parts that are mostly moving and FG increase the accuracy rate of the other parts more such as the stomach.
Now we made our enriched descriptor and here we will apply Fisher Kernel representation.