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

Abnormal Activity Detection Based on Dense Trajectory Alignment and Motion Influence Descriptor in Crowded Scenes

针对现有异常活动检测算法对拥挤场景下的目标跟踪和描述能力不足的问题,文中提出基于密集轨迹对准及其运动影响描述符的算法,捕捉视频目标运动的关键信息.密集轨迹保证对视频运动目标的有效提议,沿着轨迹的方向提取与轨迹对准的运动影响描述符.最后提出完整框架,准确检测全局和局部的异常活动.在UCSD公共数据集上的实验证明文中方法性能较优.

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.