Event Detection in Video Sequences: Challenges and Perspectives (original) (raw)
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A New System for Event Detection from Video Surveillance Sequences
Advanced Concepts for …, 2010
In this paper, we present an overview of a hybrid approach for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection.We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in the context of feature extraction, events learning and detection in large collection of video surveillance dataset.
Event detection in surveillance videos: a review
Multimedia Tools and Applications
Since 2008, a variety of systems have been designed to detect events in security cameras. There are also more than a hundred journal articles and conference papers published in this field. However, no survey has focused on recognizing events in the surveillance system. Thus, motivated us to provide a comprehensive review of the different developed event detection systems. We start our discussion with the pioneering methods that used the TRECVid-SED dataset and then developed methods using VIRAT dataset in TRECVid evaluation. To better understand the designed systems, we describe the components of each method and the modifications of the existing method separately. We have outlined the significant challenges related to untrimmed security video action detection. Suitable metrics are also presented for assessing the performance of the proposed models. Our study indicated that the majority of researchers classified events into two groups on the basis of the number of participants and th...
Event Detection and Analysis from Video Streams
We present a system for event detection and analysis from video streams. Our approach is based on a detection and tracking module which extracts moving objects trajectories from a video stream. These trajecto-ries, together with a rough description of the scene, are then used by the behavior inference module in order to recognize and classify object motion. The hierarchical tasks are performed on a buffered set of frames in order to provide accurate results by taking into account the temporal coherence of moving objects.
A Hybrid Method for Object Identification and Event Detection in Video
IEEE, 2013
Video event detection (VED) is a challenging task especially with a large variety of objects in the environment. Even though there exist numerous algorithms for event detection, most of them are unsuitable for a typical consumer purpose. A hybrid method for detecting and identifying the moving objects by their color and spatial information is presented in this paper. In tracking multiple moving objects, the system makes use of motion of changed regions. In this approach, first, the object detector will look for the existence of objects that have already been registered. Then the control is passed on to an event detector which will wait for an event to happen which can be object placement or object removal. The object detector becomes active only if any event is detected. Simple training procedure using a single color camera in HSV color space makes it a consumer application. The proposed model has proved to be robust in various indoor environments and different types of background scenes. The experimental results prove the feasibility of the proposed method.
Event detection in video using motion analysis
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams - ARTEMIS '10, 2010
Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable of processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms to perform automatic detection of specific events in video sequences. This paper presents a surveillance system based on motion analysis and introduces the idea of event probability zones. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction in a predefined context or running in the scene; the results showed precision greater than 50% and recall greater than 80%.
Behavior and event detection for annotation and surveillance
… , 2008. CBMI 2008. …, 2008
Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To achieve this goal, the paper presents results in automatic event detection in surveillance videos, and a distributed application framework for supporting these methods. Results in motion analysis for static and moving cameras, automatic fight detection, shadow segmentation, discovery of unusual motion patterns, indexing and retrieval will be presented. These applications perform real time, and are suitable for real life applications. CBMI 2008
Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance
EURASIP Journal on Image and Video Processing, 2011
Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearance ambiguity, and occlusion. In this work, we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge, split, walk, run, local dispersion, and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed.
Automatic detection of events and tracking of moving objects in video sequences
Digital video is being used widely in a variety of applications such as surveillance and security. Big amount of video in surveillance and security requires systems capable to process video automatically to detect events and track moving objects to alleviate the load on humans and enable preventive actions when events are detected . our paper focuses to develop an intelligent visual surveillance system to replace the traditional passive video surveillance that is proving ineffective as the number of cameras exceeds the capability of human operators to monitor them, and it is able to track objects within a maximum solid angle speed which is measured at about 0.3 to 0.2 radian per second, further it also depends on the complexity of the system and the processor speed as well.
Informedia@TRECVID 2011: Surveillance Event Detection
2011
This paper presents a generic event detection system evaluated in the Surveillance Event Detection (SED) task of TRECVID 2011 campaign. We investigate a generic statistical approach with spatio-temporal features applied to seven event classes, which were defined by the SED task. This approach is based on local spatio-temporal descriptors, which is named as MoSIFT and generated by pair-wise video frames. Visual vocabularies are generated by cluster centers of MoSIFT features, which were sampled from the event part video clips. We also estimated the spatial distribution of actions by over generated person detection and background subtraction. Different slide window sizes and steps were adopted for different events by events’ duration prior. Several sets of one-against-all action classifiers were trained using cascade non-linear SVMs and Random Forest, which could improve the classification performance in unbalanced data just like the SED datasets. 9 runs results were presented with va...
2008
We have developed and evaluated three generalized systems for event detection. The first system is a simple brute force search method, where each space-time location in the video is evaluated by a binary decision rule on whether it contains the event or not. The second system is build on top of a head tracker to avoid costly brute force searching. The decision stage is a combination of state of the art feature extractors and classifiers. Our third system has a probabilistic framework. From the observations, the pose of the people are estimated and used to determine the presence of event. Finally we introduce two ad-hoc methods that were designed to specifically detect OpposingFlow and TakePicture events. The results are promising as we are able to get good results on several event categories, while for all events we have gained valuable insights and experience.