Carried Object Recognition via Location Relation with Body Parts
Jun Piao, Tetsuo Inoshita, Kota Iwamoto · 2019
We propose a robust carried object recognition method from surveillance video. Though tremendous strides have been made in the object recognition by using the appearance-based method such as Faster R-CNN, YOLO, etc., recognizing carried object classes is still a difficult task. It is because the carried objects in surveillance video are of small size, low-resolution, occluded by people. To address the problem, we introduce prior knowledge of the location relation between carried objects and human body parts to improve the performance of the appearance-based method. Specifically, for the extracted human region, we detect human body parts and use prior knowledge to estimate the probability distribution of the existence of each class of carried objects. We update the score results of the appearance-based method by applying the probability distribution. In this way, we can reduce false positives and enhance confidence score of true positives. We evaluate the proposed method on four datasets and show that the proposed method outperforms Faster R-CNN by 6% in terms of average F-measure.