Moving-Object Intrusion Detection Based on Retinex-Enhanced Method
Chao Ho Chen, Tsong Yi Chen, Yu Chun Lin, Wu Chih Hu · 2014
This paper is dedicated to moving-object intrusion detection in all-weather conditions based on Retinex-enhanced approach. The proposed method mainly consists of image enhancement and intrusion detection for moving-objects. A Retinex-enhanced approach is developed for reducing the high-lighting's affection at nighttime environment. Then, the frame-difference and morphological processing are employed to remove noisy areas caused by high-lighting, shadow, and raindrops for generating the initial moving-object's mask. To extract a more complete moving-object, a refinement processing based on intensity analysis is introduced to remove ground-light (caused by car lights) areas generated in the raining situation. Finally, intrusion is judged by checking whether a moving object is crossing a predefined detection line or not. Experimental results show that the proposed method can provide an intrusion-detection rate of over 97% at daytime, over 90% at nighttime, and over 97% in the raining situation.