拥挤场景下基于密集轨迹对准及其运动影响描述符的异常活动检测

doi:10.16451/j.cnki.issn1003-6059.201805009

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Abstract Aiming at the defects of existing anomaly activity detection algorithms in terms of target tracking and description in crowded scenes, an algorithm based on dense trajectory alignment and motion influence descriptor is proposed to capture the key information of motion of video objects. Firstly, dense trajectory guarantees a valid proposal of video motion object. Then, the dense trajectory-aligned motion influence descriptor is extracted along the trajectory direction. Finally, an overall framework is developed to detect both global and local abnormal activities accurately. Experiments on UCSD public dataset prove that the proposed method outperforms other methods.

Key words： Video Surveillance Video Surveillance Abnormal Activity Detection Abnormal Activity Detection Dense Trajectory Alignment Dense Trajectory Alignment Motion Influence Descriptor Motion Influence Descriptor

Received: 31 January 2018

ZTFLH:

TP 391

Corresponding Authors: YANG Xingming, Ph.D. candidate, associate professor. His research interests include computer control, internet of things, image processing and machine learning.

About author:: HU Jun, master student. His research interests include image processing and machine learning.

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Cite this article:

YANG Xingming,YANG Xingming,HU Jun等. Abnormal Activity Detection Based on Dense Trajectory Alignment and Motion Influence Descriptor in Crowded Scenes[J]. , 2018, 31(5): 470-476.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201805009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I5/470

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