Robust real-time pedestrian detection in surveillance videos

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• 作者：Domonkos Varga ; Tamás Szirányi
• 关键词：Video surveillance ; Pedestrian detection ; Feature extraction
• 刊名：Journal of Ambient Intelligence and Humanized Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：8
• 期：1
• 页码：79-85
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Computational Intelligence; Artificial Intelligence (incl. Robotics); Robotics and Automation; User Interfaces and Human Computer Interaction;
• 出版者：Springer Berlin Heidelberg
• ISSN：1868-5145
• 卷排序：8

文摘

Detecting different categories of objects in an image and video content is one of the fundamental tasks in computer vision research. Pedestrian detection is a hot research topic, with several applications including robotics, surveillance and automotive safety. We address the problem of detecting pedestrians in surveillance videos. In this paper, we present a new feature extraction method based on Multi-scale Center-symmetric Local Binary Pattern operator. All the modules (foreground segmentation, feature pyramid, training, occlusion handling) of our proposed method are introduced with its details about design and implementation. Experiments on CAVIAR and other sequences show that the presented system can detect pedestrians in real-time effectively and accurately in surveillance videos.