Violent action represents a threat to public security, thus intelligent violence detection became one of the important and challenging topics in video surveillance scenarios for this reason there is a growing appeal of video-surveillance systems. Hence, it’s mandatory for the detection of violent or abnormal activities to avert any casualties which could cause any damages. Distinctly, in this paper, it is possible to create a network to learn spatial-temporal information on all subjects of violence rather than going through each concept separately. In order to construct a new concept for violence detection system, we rely on a strategy of a dynamic frame skipping to reduce the complexity of calculation. However, following the regions of interest in the frame, the overall complexity of the calculation is decreased. Withal, the History of Binary Motion Image for n successive images is used for features extraction to facilitate to model the human behaviors. Then, the biggest regions of interest are extracted in order to find the maximum component represented violence action. Finally, deep neural networks involve three stacked Autoencoders and a Softmax are adopted as an exterior layer for classification.
The video surveillance is one of the key areas in computer vision researches. The scientific challenge in this field involves the implementation of automatic systems to obtain detailed information about individuals and groups behaviors. In particular, the detection of abnormal movements of groups or individuals requires a fine analysis of frames in the video stream. In this article, we propose a new method to detect anomalies in crowded scenes. We try to categorize the video in a supervised mode accompanied by unsupervised learning using the principle of the autoencoder. In order to construct an informative concept for the recognition of these behaviors, we use a technique of representation based on the superposition of human silhouettes. The evaluation of the UMN dataset demonstrates the effectiveness of the proposed approach.
Currently, there are several fall detection systems based on video analysis. However, these systems have not yet reached the desired level of appropriateness and robustness. To reduce the risk of falling in insecure environments, a new method is developed in this paper to detect and predict human fall detection. We adopt, in this approach, a Block Matching motion estimation algorithm based on acceleration and changes of the human body silhouette area, which are obtained from a single surveillance camera. It presents an algorithm to accelerate the fall detection system on based on a local adjustment of the velocity field.
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