A Co-occurrence Background Model with Hypothesis on Degradation Modification for Object Detection in Strong Background Changes
Wenjun Zhou, Shun’ichi Kaneko, Manabu Hashimoto, Yutaka Satoh, Dong Liang · 2018
Object detection has become an indispensable part of video processing and current background models are sensitive to background changes. In this paper, we propose a novel background model using an algorithm called Co-occurrence Pixel-block Pairs (CPB) against background changes, such as illumination changes and background motion. We utilize the co-occurrence “pixel to block” structure to extract the spatial-temporal information of each pixel to build background model, and then employ an efficient evaluation strategy to identify the current state of each pixel, which is named as correlation dependent decision function. Furthermore, we also introduce a Hypothesis on Degradation Modification (HoD) into CPB structure to reinforce the robustness of CPB. Experimental results obtained from the dataset of the PETS 2001, AIST-Indoor, SBMnet and CDW-2012 databases show that our models can detect objects robustly in strong background changes.