Pattern Recognition ( IF 8 ) Pub Date : 2021-07-12 , DOI: 10.1016/j.patcog.2021.108146 Shaohua Wan 1, 2 , Songtao Ding 3 , Chen Chen 4
In the Internet of Things enabled intelligent transportation systems, a huge amount of vehicle video data has been generated and real-time and accurate video analysis are very important and challenging work, especially in situations with complex street scenes. Therefore, we propose edge computing based video pre-processing to eliminate the redundant frames, so that we migrate the partial or all the video processing task to the edge, thereby diminishing the computing, storage and network bandwidth requirements of the cloud center, and enhancing the effectiveness of video analyzes. To eliminate the redundancy of the traffic video, the magnitude of motion detection based on spatio-temporal interest points (STIP) and the multi-modal linear features combination are presented which splits a video into super frame segments of interests. After that, we select the key frames from these interesting segments of the long videos with the design and detection of the prominent region. Finally, the extensive numerical experimental verification results show our methods are superior to the previous algorithms for different stages of the redundancy elimination, video segmentation, key frame selection and vehicle detection.
中文翻译:
边缘计算支持视频分割,用于车联网中的实时交通监控
在物联网智能交通系统中,产生了大量的车辆视频数据,实时准确的视频分析是非常重要和具有挑战性的工作,尤其是在街道场景复杂的情况下。因此,我们提出基于边缘计算的视频预处理来消除冗余帧,从而将部分或全部视频处理任务迁移到边缘,从而降低云中心的计算、存储和网络带宽需求,增强视频分析的有效性。为了消除交通视频的冗余,提出了基于时空兴趣点(STIP)和多模态线性特征组合的运动检测幅度,将视频分割成感兴趣的超帧片段。之后,我们通过突出区域的设计和检测,从长视频的这些有趣片段中选择关键帧。最后,大量的数值实验验证结果表明,我们的方法在冗余消除、视频分割、关键帧选择和车辆检测的不同阶段都优于以前的算法。