MBO:Surveillance Video Synopsis Based on Multi-Objective Balance Optimization
Video synopsis,which can greatly compress video length while preserving complete object motion information,has received widespread attention in both academic and industrial cir-cles.However,existing synopsis methods cannot accurately preserve the interactive behaviors between objects and have difficulties in balancing compression and collisions,which seriously hin-ders the performance improvement and practical application of video synopsis.To address this is-sue,this paper proposes a surveillance video synopsis method based on multi-objective balance optimization(MBO).Firstly,a method for judging interactive behaviors based on the number of interactive frames and dynamic threshold comparison is proposed to form multi-objective units,combining the movement direction of the object in each frame and using dynamic thresholds to improve the accuracy of interaction behavior judgment.Secondly,the collision matrix and inser-tion position ratio are defined to record target collisions and the depth of insertion positions,re-spectively.Then,a dynamic balancing method between compression and collisions is proposed to optimize the rearrangement of objects,can greatly compress video length while reducing object collisions.Finally,the video background and rearranged objects are fused to generate the synop-sis video.Experimental results on multiple datasets such as VISOR,CAVIAR,and KTH show that compared with current mainstream methods,our method improves the F-score of preserving interactivity by up to 0.472 and can effectively balance compression and object collisions.
video synopsismulti-objectivebalance optimizationinteractive behavior
万方数据
维普
