Abnormal Object Detection Using Feedforward Model and Sequential Filters
Jiman Kim, Bong-Nam Kang, Hai Wang, Daijin Kim · 2012
Abnormal object detection and discrimisnation is a critical research area for vision-based surveillance systems. This paper proposes a novel algorithm for the detection and discrimination of abnormal objects, such as abandoned and stolen objects. The proposed algorithm consists of three stages and three different filters. The three stages cooperate with each other using the feedforward model to enhance detection and discrimination performance, while the sequential filters efficiently reject falsely detected regions using three categories of information. The results of experiments conducted using public datasets indicate that the proposed algorithm is more accurate and has a lower false alarm ratio than the existing system.