Accurate Static Region Classification Using Multiple Cues for ARO Detection

Abstract

This letter proposes an accurate static region classification for detecting abandoned or removed objects (ARO) using multiple cues. Most existing ARO detection approaches show many falsely detected static regions and low ARO detection performance in real situations because they use single cue and a number of pre-defined threshold values. The proposed method presents multiple cues as intensity, motion, and shape to characterize the true static regions and classifies their candidates into true/false static regions using a SVM classifier, which avoids any dependency on pre-defined threshold values. Experimental results show that the proposed method achieved better ARO detection accuracy and lower false detection rate than the existing methods. In addition, the proposed method can be utilized to several practical applications such as illegal parking detection, garbage throwing detection, thief detection, forest fire detection, and camouflaged solider detection.