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片段-自适应的监控视频浓缩

Segmentation-Adaptive Surveillance Video Synopsis

  • 摘要: 为解决现有视频浓缩在复杂场景下存在的轨迹不完整、交互行为难以保留等问题, 提出一种片段-自适应的监控视频浓缩方法. 首先, 提出一种视频划分模块, 对输入视频逐帧进行拥挤度检测, 采用自适应阈值将视频划分为稀疏和拥挤片段, 并将被中断的轨迹进行链接形成延展拥挤片段; 其次, 设计交互行为判别模块, 结合空间距离和视频自适应阈值判别并保留目标之间的交互行为; 最后, 提出片段-自适应重排模块, 综合碰撞约束、空间占比约束、交互约束和时序约束生成最优时间标签, 进而融合背景生成浓缩视频. 在数据集 VISOR, BEHAVE和 CAVIAR上的实验结果表明, 该方法比当前主流方法在帧压缩率和碰撞率上分别降低了 0.136 和 0.011, 计算时间减少了 120.03 s.

     

    Abstract: To solve the problems of existing video synopsis methods such as incomplete tracks and difficulty to retain interactive behaviors in complex scenes, a segmentation-adaptive video synopsis method is proposed. Firstly, a video segmentation module is proposed, which detects the crowding degree of each frame of the input video, and divides the video into sparse and crowded segmentation using self-adaption threshold, and links the interrupted track to form extending crowded segmentation. Secondly, an interactive behavior judgment module is designed, which combines spatial distance and video self-adaption threshold to comprehensively judge and retain interactive behavior between objects. Finally, a segmentation-adaptive rearrangement module is proposed, which combines collision constraints, space proportion constraint, interactive constraints and temporal constraints to generate the optimal time tag, and fuses the background to generate synopsis video. Experimental results on public dataset VISOR, BEHAVE and CAVIAR show that compared with the current mainstream methods, the proposed method reduces 0.136 and 0.011 frame compression rate and collision rate respectively, and the time cost is reduced by 120.03 s.

     

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