Les manifestations violentes

Abstract. An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transferable Belief Model (TBM) framework for action recognition. The TBMbased modelling and fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. Action recognition is performed by a multilevel analysis. The sequencing is exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of jumps: high jump, pole vault, triple jump and long jump.

Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable BeliefModel

We present an automatic human shape-motion analysis method based on a fusion architecture for human action and activity recognition in athletic videos. Robust shape and motion features are extracted from human detection and tracking. The features are combined within the Transferable Belief Model (TBM framework for two levels of recognition. The TBM-based modelling of the fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions are roughly recognized. Then, in a fine step, an action sequence recognition method is used to discriminate activities. Belief on actions are made smooth by a Temporal Credal Filter and action sequences, i.e. activities, are recognized using a state machine, called belief scheduler, based on TBM. The belief scheduler is also exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of actions (running, jumping, falling and standing) and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activities and feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposed recognition approach based on TBM

Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided

Human motion analysis and simulation tools: a survey

Computational systems to identify objects represented in image sequences and tracking their motion in a fully automatic manner, enabling a detailed analysis of the involved motion and its simulation are extremely relevant in several fields of our society. In particular, the analysis and simulation of the human motion has a wide spectrum of relevant applications with a manifest social and economic impact. In fact, usage of human motion data is fundamental in a broad number of domains (e.g.: sports, rehabilitation, robotics, surveillance, gesture-based user interfaces, etc.). Consequently, many relevant engineering software applications have been developed with the purpose of analyzing and/or simulating the human motion. This chapter presents a detailed, broad and up to date survey on motion simulation and/or analysis software packages that have been developed either by the scientific community or commercial entities. Moreover, a main contribution of this chapter is an effective framework to classify and compare motion simulation and analysis tools

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

VCoach: A Customizable Visualization and Analysis System for Video-based Running Coaching

Videos are accessible media for analyzing sports postures and providing feedback to athletes. Existing video-based coaching systems often present feedback on the correctness of poses by augmenting videos with visual markers either manually by a coach or automatically by computing key parameters from poses. However, previewing and augmenting videos limit the analysis and visualization of human poses due to the fixed viewpoints, which confine the observation of captured human movements and cause ambiguity in the augmented feedback. Besides, existing sport-specific systems with embedded bespoke pose attributes can hardly generalize to new attributes; directly overlaying two poses might not clearly visualize the key differences that viewers would like to pursue. To address these issues, we analyze and visualize human pose data with customizable viewpoints and attributes in the context of common biomechanics of running poses, such as joint angles and step distances. Based on existing literature and a formative study, we have designed and implemented a system, VCoach, to provide feedback on running poses for amateurs. VCoach provides automatic low-level comparisons of the running poses between a novice and an expert, and visualizes the pose differences as part-based 3D animations on a human model. Meanwhile, it retains the users' controllability and customizability in high-level functionalities, such as navigating the viewpoint for previewing feedback and defining their own pose attributes through our interface. We conduct a user study to verify our design components and conduct expert interviews to evaluate the usefulness of the system

HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition

Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data

A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work

In recent years, there has been a rapid development in web users and sufficient bandwidth. Internet connectivity, which is so low cost, makes the sharing of information (text, audio, and videos) more common and faster. This video content needs to be analyzed for prediction it classes in different purpose for the users. Many machines learning approach has been developed for the classification of video to save people time and energy. There are a lot of existing review papers on video classification, but they have some limitations such as limitation of the analysis, badly structured, not mention research gaps or findings, not clearly describe advantages, disadvantages, and future work. But our review paper almost overcomes these limitations. This study attempts to review existing video-classification procedures and to examine the existing methods of video-classification comparatively and critically and to recommend the most effective and productive process. First of all, our analysis examines the classification of videos with taxonomical details, the latest application, process, and datasets information. Secondly, overall inconvenience, difficulties, shortcomings and potential work, data, performance measurements with the related recent relation in science, deep learning, and the model of machine learning. Study on video classification systems using their tools, benefits, drawbacks, as well as other features to compare the techniques they have used also constitutes a key task of this review. Lastly, we also present a quick summary table based on selected features. In terms of precision and independence extraction functions, the RNN (Recurrent Neural Network), CNN (Convolutional Neural Network) and combination approach performs better than the CNN dependent method