Video Synopsis is a smart and efficient solution to summarize a long duration of surveillance video into short. Most of the video synopsis techniques are not suitable to address complex situations like changes in illumination, dynamic background, camera jitter, etc. These techniques firmly depend on the preprocessing results of foreground extraction and multiple objects tracking. 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. 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, 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. The performance of the proposed scheme is evaluated through extensive simulations and compared with that of the benchmark schemes. 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. Overall analysis and experimental evaluations demonstrate that our proposed scheme outperforms the other competing schemes in terms of both the quantitative and qualitative measures. Finally, the proposed model can be substantially employed in the generation of off-line video synopsis, which is potentially applicable to video surveillance applications for smart cities.

视频提要是一种聪明而有效的解决方案，可以将长时间的监视视频汇总为简短。大多数视频概要技术都不适合解决复杂的情况，例如光照变化，动态背景，摄像机抖动等。这些技术完全取决于前景提取和多对象跟踪的预处理结果。此外，优化过程是降低运动对象之间的碰撞率的重要阶段，在该阶段，广泛使用的模拟退火（SA）通常会遇到收敛速度慢，计算开销高的问题。考虑到上述事实以进行特征提取，我们制定了一种前景提取方案，探索了生成对抗网络（mFS-GAN）中多帧和多尺度的概念。进一步，通过将SA和Gray Wolf Optimizer（GWO）混合，提出了一种名为HGWOSA的优化算法，以确保全局最优结果且计算开销较低。通过广泛的仿真评估了所提出方案的性能，并与基准方案进行了比较。实验是使用IIIT Bhubaneswar上的一些标准监视视频数据集（ChangeDetection.Net，MIT Surveillance Dataset和UMN Dataset）以及一个自生成的监视视频进行的。总体分析和实验评估表明，我们提出的方案在数量和质量方面均优于其他竞争方案。最后，所提出的模型可以在离线视频概要的生成中得到实质性的应用，