부산대학교 도서관

Object detection is an important research area in video surveillance systems, aimed at identifying and locating target objects within recorded scenes. Various object detectors fail when partial occlusion occurs in which only some features of the objects are visible due to overlapped bounding boxes. This situation can result in miscounting of the objects and misaligning the bounding boxes leading to localization loss. To address these problems, we have proposed a geometric-based axis-aligned bounding box method with occlusion prior conditions to estimate the location of overlapped bounding boxes with a single viewpoint. Firstly, the proposed method detects the closest points of the detected bounding boxes by extracting geometric features namely the width, height, and area of the detected objects. Secondly, occlusion prior condition is used to detect the partial occlusion and compute the overlapped area under different levels of occlusions such as (i) 20–40% and (ii) 40–70%. The performance of the proposed method has been tested on two benchmark datasets: Highway and PETS 2006, both containing outdoor video frames. The experimental results show that the proposed method can detect the objects under partial occlusion which are approximately 65% occluded with 92.7% precision in the Highway dataset and 85.1% precision in the PETS2006 dataset. Also, it has been observed that the bounding box localization loss of the proposed method has been improved by 1.76% in the Highway dataset and 2% in the PETS2006 dataset by generating the correct aligned bounding boxes on the detected objects, especially in the case of partial occlusion.