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On-line abnormality detection in video without the use of object detection and tracking is a desirable task in surveillance.We address this problem for the case when labeled information about normal events is limited and information about abnormal events is not available. We formulate this problem as a one-class classification, where multiple local novelty classifiers (detectors) are used to first...
Vision algorithms face many challenging issues when it comes to analyze human activities in video surveillance applications.For instance, occlusions makes the detection and tracking of people a hard task to perform. Hence advanced and adapted solutions are required to analyze the content of video sequences. We here present a people detection algorithm based on a hierarchical tree of Histogram of Oriented...
In this paper an improved real time algorithm for detecting pedestrians in surveillance video is proposed. The algorithm is based on people appearance and defines a person model as the union of four models of body parts. Firstly, motion segmentation is performed to detect moving pixels. Then, moving regions are extracted and tracked. Finally, the detected moving objects are classified as human or...
In this paper, we propose a new framework in pedestrian detection using a two-step classification algorithm, which is a ??coarse to fine?? course. The framework consists of a full-body detection (FBD) step and a head-shoulder detection (HSD) step. The FBD step uses fusion of Haar-like and HOG features to get better performance, and the HSD step utilizes edgelet features for classification and detection...
This paper starts from the idea of automatically choosing the appropriate thresholds for a shadow detection algorithm. It is based on the maximization of the agreement between two independent shadow detectors without training data. Firstly, this shadow detection algorithm is described and then, it is adapted to analyze video surveillance sequences. Some modifications are introduced to increase its...
The paper introduces a video surveillance and event detection framework and application for semi-supervised surveillance use. The systempsilas intended use is in automatic mode on camera feeds that are not actively watched by surveillance personnel, and should raise alarms when unusual events occur. We present the current detector filters, and the extendable modular interface. Filters include local...
Laser radar has enjoyed significant advances over the past decade. Novel sensor topologies, compact laser illuminators, and advanced signal processing have enabled the construction of low power, portable 2-D and 3-D laser vision systems. The applications of such systems range from surveillance, targeting, weapons guidance, and remote scene measurement, to target identification and atmospheric characterization...
A scanning window type pedestrian detector is presented that uses both appearance and motion information to find walking people in surveillance video. We extend the work of Viola, Jones and Snow (2005) to use many more frames as input to the detector thus allowing a much more detailed analysis of motion. The resulting detector is about an order of magnitude more accurate than the detector of Viola,...
In this paper we propose a real-time algorithm for detecting and tracking moving objects in a video sequence. Based on the on-line boosting framework, our algorithm is able to detect an object as a member of a class, e.g. pedestrian, then a specific model for each instance of the class can be built on-line allowing at the same time robust tracking and recognition of the particular instance as it leaves...
Which cues to be used in describing pedestrian are the key to detect pedestrian with Adaboost algorithm. In this paper we presented 4 triangle cues and 16 complex cues by researching on pedestrianpsilas figures and proposed the way to calculate these cuespsila sum. To evaluate this calculating method, we proved the relation of iterative times with error and time spending. Compared with rectangle and...
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