Foreground detection is one of the most important and fundamental tasks in many computer vision applications such as real-time video surveillance. Although there have been many efforts to find solutions to this problem, many obstacles such as illumination changes, noises, dynamic backgrounds, and computational complexities have prevented them from being used in real surveillance systems. In this paper, to alleviate these inherent limitations of conventional methods, we propose a fast illumination-robust foreground detection (FIFD) system that provides robustness against illumination variations and noises from various real circumstances with an efficient computational scheme. In contrast to the conventional approaches, our method focuses on efficiently formulating the foreground object detection system by leveraging a foreground candidate region detection and hierarchical distribution map. Specifically, our approach consists of three parts. First, for a query image, foreground candidates are detected by fusing multiple methods. The existence and the block size of the foreground object are determined through the use of the foreground continuity. Second, the foreground block is found from the estimated distribution map and then detected from the extracted valid blocks. Finally, with a labeling scheme, the foreground is detected. To intensively evaluate our approach compared to the conventional methods, we use the publicly available I2R and traffic datasets, and we build a novel electron multiplying charge-coupled device foreground detection benchmark taken in an environment with light lower than 10lux. Experimental results show that our approach provides satisfactory performance compared to the state-of-the-art methods even under very challenging circumstances. Furthermore, our approach is very efficient in that it takes only approximately 31 ms per frame.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence