Abstract
The article presents a hardware implementation of the foreground object detection algorithm PBAS (Pixel-Based Adaptive Segmenter) with a scene analysis module. A mechanism for static object detection is proposed, which is based on consecutive frame differencing. The method allows to distinguish stopped foreground objects (e.g. a car at the intersection, abandoned luggage) from false detections (so-called ghosts) using edge similarity. The improved algorithm was compared with the original version on popular test sequences from the changedetection.net dataset. The obtained results indicate that the proposed approach allows to improve the performance of the method for sequences with the stopped objects. The algorithm has been implemented and successfully verified on a hardware platform with Virtex 7 FPGA device. The PBAS segmentation, consecutive frame differencing, Sobel edge detection and advanced one-pass connected component analysis modules were designed. The system is capable of processing 50 frames with a resolution of 720 × 576 pixels per second
In this paper, we extract for key objects of motion video images (mainly people), then do researches with the technology of object detection and tracking. First, we extract moving object edge and foreground object template with the use of the method of edge detection, and then compare the resulting object template with the original image, thus dye the positions with the colour of the corresponding point coordinates in the original image which belongs to the extent of template filled region. Simulation results show that the proposed algorithm has some advantages and robustness and could meet project needs.
Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module
