HSAJAYA: An Improved Optimization Scheme for Consumer Surveillance Video Synopsis Generation

Abstract

Video Surveillance is an active area of research and provides promising security measures for consumer applications. To ease consumer surveillance investigations, Video Synopsis (VS) serves as a powerful tool to assess hours of video in a shorter retro of time by projecting multiple objects concurrently. The optimization module in VS framework is considered to be a key module, yet to date, only traditional optimization techniques have been addressed for energy minimization. Amongst these, simulated annealing (SA) has been broadly employed to produce global optimal solution without getting trapped in local minima. However, the convergence time of SA is quite high as the next state is chosen randomly to achieve real-time performance. This article presents an improved energy minimization scheme using hybridization of SA and JAYA algorithm to achieve global optimal solution with faster convergence rate. The weights associated with the energy function are computed using analytic hierarchy process (AHP) instead of heuristic selection. From experimental evaluations and analysis, it is seen that the proposed scheme exhibits superior performance to minimize the overall energy cost with lesser computational time. The proposed scheme has a potential to quickly review consumer surveillance video data in a smart and efficient way.