Video synopsis is an effective way to easily summarize long-recorded surveillance videos.The omnidirectional view allows the observer to select the desired fields of view(FoV)from the different FoVavailable for spheri...Video synopsis is an effective way to easily summarize long-recorded surveillance videos.The omnidirectional view allows the observer to select the desired fields of view(FoV)from the different FoVavailable for spherical surveillance video.By choosing to watch one portion,the observer misses out on the events occurring somewhere else in the spherical scene.This causes the observer to experience fear of missing out(FOMO).Hence,a novel personalized video synopsis approach for the generation of non-spherical videos has been introduced to address this issue.It also includes an action recognition module that makes it easy to display necessary actions by prioritizing them.This work minimizes and maximizes multiple goals such as loss of activity,collision,temporal consistency,length,show,and important action cost respectively.The performance of the proposed framework is evaluated through extensive simulation and compared with the state-of-art video synopsis optimization algorithms.Experimental results suggest that some constraints are better optimized by using the latest metaheuristic optimization algorithms to generate compact personalized synopsis videos from spherical surveillance videos.展开更多
针对云台网络摄像机监控系统,提出一种基于摄像机视频流的全景图生成算法,以构建更大的监控场景。根据帧间重叠区域的大小选取关键帧,进行柱面投影,利用计算性能优越的SURF(Speeded Up Robust Features,加速鲁棒性特征)算法对所选取的...针对云台网络摄像机监控系统,提出一种基于摄像机视频流的全景图生成算法,以构建更大的监控场景。根据帧间重叠区域的大小选取关键帧,进行柱面投影,利用计算性能优越的SURF(Speeded Up Robust Features,加速鲁棒性特征)算法对所选取的关键帧进行特征点提取,使用基于哈希映射的特征点匹配算法加快特征点的匹配,并结合RANSAC(RANdom SAmple Consensus,随机抽样一致)算法剔除误匹配,估计关键帧之间的变换关系。实验结果表明,该方法能较好实现视频序列的快速拼接,鲁棒性强,具有较高的实用价值。展开更多
文摘Video synopsis is an effective way to easily summarize long-recorded surveillance videos.The omnidirectional view allows the observer to select the desired fields of view(FoV)from the different FoVavailable for spherical surveillance video.By choosing to watch one portion,the observer misses out on the events occurring somewhere else in the spherical scene.This causes the observer to experience fear of missing out(FOMO).Hence,a novel personalized video synopsis approach for the generation of non-spherical videos has been introduced to address this issue.It also includes an action recognition module that makes it easy to display necessary actions by prioritizing them.This work minimizes and maximizes multiple goals such as loss of activity,collision,temporal consistency,length,show,and important action cost respectively.The performance of the proposed framework is evaluated through extensive simulation and compared with the state-of-art video synopsis optimization algorithms.Experimental results suggest that some constraints are better optimized by using the latest metaheuristic optimization algorithms to generate compact personalized synopsis videos from spherical surveillance videos.
