Name
Abstract | ||
|---|---|---|
Video surveillance cameras capture huge amount of data 24 hours a day. However, most of these videos contain redundant data which make the process difficult for browsing and analysis. A significant amount of research findings have been made in summarization of recorded video, but such schemes do not have much impact on video surveillance applications. On the contrary, video synopsis is a smart technology that preserves all the activities of every single object and projects them concurrently in a condensed time. The energy minimization module in video synopsis framework plays a vital role, which in turn minimizes the activity loss, number of collision and temporal consistency cost. In most of the reported schemes, Simulated Annealing (SA) algorithm is employed to solve the energy minimization problem. However, it suffers from slow convergence rate resulting in a high computational load to the system. In order to mitigate this issue, this article presents an improved energy minimization scheme using hybridization of SA and Teaching Learning based Optimization (TLBO) algorithms. The suggested framework for static surveillance video synopsis generation consists of four computational modules, namely, Object detection and segmentation, Tube formation, Optimization, and finally Stitching and the central focus is on the optimization module. Thus, the present work deals with an improved hybrid energy minimization problem to achieve global optimal solution with reduced computational time. The motivation behind hybridization (HSATLBO) is that TLBO algorithm has the ability to search rigorously, ensuring to reach the optimum solution with less computation. On the contrary, SA reaches the global optimum solution, but it may get disarrayed and miss some critical search points. Exhaustive experiments are carried out and results compared with that of benchmark schemes in terms of minimizing the activity, collision and temporal consistency costs. All the experiments are conducted on five widely used videos taken from standard surveillance video data set (PETS 2001, MIT Surveillance Dataset, ChangeDetection.Net, PETS 2006 and UMN Dataset) as well as one real generated surveillance video from the IIIT Bhubaneswar Surveillance Dataset. To make a fair comparison, additionally, performance of the proposed hybrid scheme to solve video synopsis optimization problem is also compared with that of the other benchmark functions. Experimental evaluation and analysis confirm that the proposed scheme outperforms other state-of-the-art approaches. Finally, the suggested scheme can be easily and reliably deployed in the off-line video synopsis generation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-019-7389-7 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Energy minimization, Optimization, Simulated annealing (SA), Teaching learning based optimization (TLBO), Video surveillance, Video synopsis | Simulated annealing,Automatic summarization,Data mining,Object detection,Computer vision,Image stitching,Computer science,Segmentation,Rate of convergence,Artificial intelligence,Optimization problem,Energy minimization | Journal |
Volume | Issue | ISSN |
79 | 7-8 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhankar Ghatak | 1 | 0 | 1.01 |
| Suvendu Rup | 2 | 11 | 5.55 |
| Banshidhar Majhi | 3 | 356 | 49.76 |
| M. N. S. Swamy | 4 | 7 | 4.51 |