| dc.description.abstract |
Video surveillance is typically used to capture moving subjects which users may be interested in from an image sequence, then applying it into different software applications for analysis and identification afterwards. These kinds of techniques are usually based on background subtraction (BS). In this paper, seven popular BS algorithms (FD, AMF, Stauffer GMM, Zivkovic GMM, KDE, Eigenbackground, Codebook) are compared and evaluated with open source video database to rate their performance. We adopted the receiver operating characteristic (ROC) curve as the evaluation metric to compare the result of BS algorithm, foreground object, with accurate ground truth data. In contrast with previous BS studies, this paper is especially focused on the complexity of implementing these methods on hardware device, like FPGA. Several properties of each algorithm will also be discussed in the article including accuracy, precision, efficiency, memory usage, and code size. The findings will provide reference for future BS algorithm applications on embedded hardware systems. Our results show that approximated median filtering is precise and performs superior in every evaluative category. Considering its ease of use and minimal memory requirements, it is a pragmatic choice for embedded systems design. Furthermore, our findings reveal no significant difference between accuracy of the results from the various BS methods used. | en_US |