Background Modeling For Object Detection Research Articles

Object detection based on spatiotemporal background models

We present a robust background model for object detection and its performance evaluation using the database of the Background Models Challenge (BMC). Background models should detect foreground objects robustly against background changes, such as “illumination changes” and “dynamic changes”. In this paper, we propose two types of spatiotemporal background modeling frameworks that can adapt to illumination and dynamic changes in the background. Spatial information can be used to absorb the effects of illumination changes because they affect not only a target pixel but also its neighboring pixels. Additionally, temporal information is useful in handling the dynamic changes, which are observed repeatedly. To establish the spatiotemporal background model, our frameworks model an illumination invariant feature and a similarity of intensity changes among a set of pixels according to statistical models, respectively. Experimental results obtained for the BMC database show that our models can detect foreground objects robustly against background changes.

Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization

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.