During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder.

This paper proposes a novel tracking framework with adaptive features and constrained labels (AFCL) to handle illumination variation, occlusion and appearance changes caused by the variation of positions. The novel ensemble classifier, including the Forward–Backward error and the location constraint is applied, to get the precise coordinates of the promising bounding boxes. The Forward–Backward error can enhance the adaptation and accuracy of the binary features, whereas the location constraint can overcome the label noise to a certain degree. We use the combiner which can evaluate the online templates and the outputs of the classifier to accommodate the complex situation. Evaluation of the widely used tracking benchmark shows that the proposed framework can significantly improve the tracking accuracy, and thus reduce the processing time. The proposed framework has been tested and implemented on the embedded system using TMS320C6416 and Cyclone Ⅲ kernel processors. The outputs show that achievable and satisfying results can be obtained.