Co-occurrence Background Model with Hypothesis on Degradation Modification for Robust Object Detection

Abstract

This paper presents a prospective background model for robust object detection in severe scenes. This background model using a novel algorithm, Co-occurrence Pixel-block Pairs (CPB), that extracts the spatiotemporal information of pixels from background and identifies the state of pixels at current frame. First, CPB realizes a robust background model for each pixel with spatiotemporal information based on a "pixel to block" structure. And then, CPB employs an efficient evaluation strategy to detect foreground sensitively, which is named as correlation dependent decision function. On the basis of this, a Hypothesis on Degradation Modification (HoD) for CPB is introduced to adapt dynamic changes in scenes and reinforce robustness of CPB to against "noise" in real conditions. This proposed model is robust to extract foreground against changes, such as illumination changes and background motion. Experimental results in different challenging datasets prove that our model has good effect for object detection.

Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

“…The proposed algorithm is doing well in detecting unusual events in the video surveillance with maintaining a low false alarm rate. However, the last years show introducing some novel algorithms for video object detecting based on statistics [16,17].…”