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With the dramatic growth of using video cameras for applications of public surveillances in recent years, detection of public threats or security issues on surveillances becomes possible nowadays. How to identify anomalous behavior from surveillance videos has been identified as an effective manner for detecting critical events in the public avenue. We in this paper discuss a new application paradigm...
In this paper, we present a new method to automatically discover recurrent activities occurring in a video scene, and to identify the temporal relations between these activities, e.g. to discover the different flows of cars at a road intersection, and to identify the traffic light sequence that governs these flows. The proposed method is based on particle-based trajectories, analyzed through a cascade...
Video surveillance systems are commonly used by security personnel to monitor and record activity in buildings, public gatherings, busy roads, and parking lots. These systems allow many cameras to be observed by a small number of trained human operators but suffer from potential operator fatigue and lack of attention due to the large amount of information provided by cameras which can distract the...
Compared to other approaches that analyze object trajectories, we propose to detect anomalous video events at three levels considering spatiotemporal context of video objects, i.e., point anomaly, sequential anomaly, and co-occurrence anomaly. A hierarchical data mining approach is proposed to achieve this task. At each level, the frequency based analysis is performed to automatically discover regular...
We propose a framework to learn scene semantics from surveillance videos. Using the learnt scene semantics, a video analyst can efficiently and effectively retrieve the hidden semantic relationship between homogeneous and heterogeneous entities existing in the surveillance system. For learning scene semantics, the algorithm treats different entities as nodes in a graph, where weighted edges between...
In this paper, we investigate the applicability of the newly proposed data clustering method, affinity propagation, in feature points clustering and the task of vehicle detection and tracking in road traffic surveillance. We propose a model-based temporal association scheme and novel preprocessing and postprocessing operations which together with affinity propagation make a quite successful method...
In recent years, many studies have focused on intelligent video surveillance system, including camera calibration, foreground region detection, moving object detection, moving object tracking and path modeling. This study used the data of the moving trajectory of a moving object as the path modeling data. However, the data may contain incorrect trajectory data, such as wrong foreground region detection...
This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal...
A new method for real-time detection and tracking of multiple moving vehicles from traffic video is proposed. This method first uses MoG and texture based model to extract foreground from the scene, then detect moving targets using a modified version of timed motion history image (tMHI), and finally uses Kalman prediction filter to track these targets, which the full moving trajectories of the targets...
In this paper, we describe an unsupervised model of activity perception by vehicles trajectories in a visual surveillance scene. We introduce a novel trajectory similarity measure based on for comparing trajectories to cluster them. Then using the result of clustering, a dynamic probabilistic network model is constructed and behavior patterns of normal vehicle's trajectories are obtained. At last,...
This paper proposes an analysis method based on movement string for behavior understanding. Trajectories are analyzed by the improved principal component analysis (PCA) method which introduces the trajectory location and direction. Trajectory location and direction are the main features of PCA for scene division and Gaussian mixture hidden Markov model. With the help of these two features, we can...
This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating...
Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion...
The traffic safety monitoring technology becomes an active research field due to the development of the city's transport system. In this paper, we design a warning method in traffic video monitoring through analyzing vehicle states. This method is based on image analysis and processing technology, extracting the information of the characteristics of the movement of vehicles through detecting and tracking...
The semantic structure of scene is important information used for interpretation of object behavior or event detection in video surveillance system. In this paper, we propose an automatic method for learning models of semantic region by analyzing the trajectories of moving objects in the scene. First, the trajectory is encoded to represent both the position of the object and its instantaneous velocity...
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