A novel deformable body partition model for MMW suspicious object detection and dynamic tracking

Abstract

In recent years, millimeter wave (MMW) imaging techniques have developed rapidly and been widely used in public security field. Traditional security techniques on MMW images mostly based on manual analysis and simple image processing, which cannot achieve real-time and accurate performance. This paper proposes a deep learning-based deformable body partition (DBP) model for detecting suspicious objects hidden in human body on MMW image dataset. Considering the characteristics of MMW security images, we combine the location information of concealed object with the corresponding human body part for object detection. According to the human body structure, 17 deformable regions are partitioned with 12 body keypoints to address body region misalignment problems of different people. By training convolutional neural network (CNN) with different body regions, the suspicious objects can be detected, together with their location information. To make full use of spatio-temporal context information of MMW image sequences, we design an object dynamic tracking method to lock the concealed targets moving from different angles’ MMW images of the same still body. Compared with conventional global-based object detection methods, the proposed method can not only accurately detect suspicious objects but also output the location information of them. Importantly, DBP model is more adaptive than the fixed coordinate-based body partition method and can automatically change the size of each body region according to each person’s body shape. Moreover, our object dynamic tracking method can utilize the positional relationship of suspicious objects in image sequences to reduce the object searching area of each image. Experimental results prove the effectiveness of the proposed method. The detection accuracy and speed on MMW images are favorable in practical application.