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.
杨兴明, 胡军. 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[J]. 模式识别与人工智能, 2018, 31(5): 470-476.
YANG Xingming, HU Jun. Abnormal Activity Detection Based on Dense Trajectory Alignment and Motion Influence Descriptor in Crowded Scenes. , 2018, 31(5): 470-476.
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2018, Vol. 31 
