Object detection through edge behavior modeling

Adin Ramirez Rivera, Mahbub Murshed, Oksam Chae

Abstract: The detection of moving objects depends on the accuracy of the model used to represent the background. Common pixel-based and naive edge-based approaches have many drawbacks in dynamic environments, e.g., false detections with noise. We propose a novel background model that encodes the background as edges, building a statistical distribution per segment that represents the edge behavior. We build the background distributions using a kernel-based approach; the moving objects are detected as the edges that deviate from the distributions. The method does adaptive thresholding to the edges, which maintains their shape and boosts the detection accuracy. Sets of gradient distributions are incorporated into the model, to determine edges that lie within the distributions, but are moving edges. The number of distributions is handled dynamically, allowing them to increase and decrease accordingly to the situation. The experiments show that the proposed method improves the detection rates, due to its robustness against illumination changes.