Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection

This paper presents a method of moving object detection through a fast background subtraction technique suitable for real-time performance in wide range of platforms. An intermittent background update using adaptive blocks individually calculates the learning rate through expected difference values. Then, coupled with a fast background subtraction process, the design achieves fast throughput with well-rounded performance. To compensate for the lagging effects of intermittent background update, an adaptation bias is devised to improve precision and recall metrics. Experiments show a versatile performance in varying scenes with overall results better than conventional techniques. The proposed method achieved a fast execution speed of up to 56 fps in PC using Full HD video. It also achieved 655 fps and 83 fps in PC and ARM core-embedded platform, respectively, using the minimum input resolution of 320 x 240. Overall, it is suitable for real-time performance applications.

Automated summary

The paper introduces a method for moving object detection using fast background subtraction suitable for real-time performance. By intermittently updating the background using adaptive blocks and employing a fast background subtraction process, the design achieves fast throughput and well-rounded performance. An adaptation bias is used to compensate for lagging effects and improve precision and recall metrics.