Overall analysis and experimental evaluations demonstrate that our proposed scheme outperforms the other competing schemes in terms of both the quantitative and qualitative measures. The experiments are carried out using some standard surveillance video dataset (ChangeDetection.Net, MIT Surveillance Dataset, and UMN Dataset) and one self-generated surveillance video at IIIT Bhubaneswar. The performance of the proposed scheme is evaluated through extensive simulations and compared with that of the benchmark schemes. Further, an optimization algorithm is proposed through the hybridization of SA and Grey Wolf Optimizer (GWO), named as, HGWOSA to ensure global optimal result with a low computing overhead. Taking these aforementioned facts into account for feature extraction, we formulate a foreground extraction scheme exploring the concept of multi-frame and multi-scale in Generative Adversarial Network (mFS-GANs). Further, the optimization process is a vital phase for the decrement of collision rate among moving objects, where the widely used Simulated Annealing (SA) usually suffers from the issue of slow convergence rate with a high computational overhead. These techniques firmly depend on the preprocessing results of foreground extraction and multiple objects tracking. Most of the video synopsis techniques are not suitable to address complex situations like changes in illumination, dynamic background, camera jitter, etc. Video Synopsis is a smart and efficient solution to summarize a long duration of surveillance video into short.

Illuminations video