Object detection using adaptive block-based background model
Wen‐Kai Tsai, Jianhui Chen, Ming‐Hwa Sheu, Chi‐Chia Sun · 2016
This paper propose an adaptable block-based background modeling and real time image object detection algorithm. In training step, we present adaptable block-based background model that uses major color number to determine the block size. This background model can reduce the memory consumption, efficiently. In detection step, we use one pixel to compare with background model. Then, it can reduce processing time. The experiment results show that we can save 33.9% memory space. Finally, we can achieve 27.25 frames per second for the benchmark video with image size 768×576.