The ant colony optimization meta-heuristic
New ideas in optimization
An Efficient Neural Network Algorithm for the p-Median Problem
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
ACM Computing Surveys (CSUR)
Parallel adaptive simulated annealing for computer-aided measurement in functional MRI analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A simulated annealing algorithm for manufacturing cell formation problems
Expert Systems with Applications: An International Journal
An object-based comparative methodology for motion detection based on the F-Measure
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A background subtraction algorithm for detecting and tracking vehicles
Expert Systems with Applications: An International Journal
Performance evaluation of object detection algorithms for video surveillance
IEEE Transactions on Multimedia
Automatic detection of musicians' ancillary gestures based on video analysis
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In computer vision, moving object detection and tracking methods are the most important preliminary steps for higher-level video analysis applications. In this frame, background subtraction (BS) method is a well-known method in video processing and it is based on frame differencing. The basic idea is to subtract the current frame from a background image and to classify each pixel either as foreground or background by comparing the difference with a threshold. Therefore, the moving object is detected and tracked by using frame differencing and by learning an updated background model. In addition, simulated annealing (SA) is an optimization technique for soft computing in the artificial intelligence area. The p-median problem is a basic model of discrete location theory of operational research (OR) area. It is a NP-hard combinatorial optimization problem. The main aim in the p-median problem is to find p number facility locations, minimize the total weighted distance between demand points (nodes) and the closest facilities to demand points. The SA method is used to solve the p-median problem as a probabilistic metaheuristic. In this paper, an SA-based hybrid method called entropy-based SA (EbSA) is developed for performance optimization of BS, which is used to detect and track object(s) in videos. The SA modification to the BS method (SA-BS) is proposed in this study to determine the optimal threshold for the foreground-background (i.e., bi-level) segmentation and to learn background model for object detection. At these segmentation and learning stages, all of the optimization problems considered in this study are taken as p-median problems. Performances of SA-BS and regular BS methods are measured using four videoclips. Therefore, these results are evaluated quantitatively as the overall results of the given method. The obtained performance results and statistical analysis (i.e., Wilcoxon median test) show that our proposed method is more preferable than regular BS method. Meanwhile, the contribution of this study is discussed.