Physiological characteristics inspired hidden human object detection model

The current target detection algorithms provide the unsatisfactory performance on the task of detecting hidden human targets. In this study, we put forward the physiological characteristics inspired hidden human object detection model considering the spatio-temporal physiological features and their interdependent relationships. The experimental results of homemade hidden human object dataset demonstrate that the proposed model generates the detection accuracy of 64%, 44%, and 54% for indoor scene, outdoor scene, and overall dataset, respectively, outperforming the YOLO v4 models and the models based on HOG, LBP, and Haar features, with at least 22% promotion in detection accuracy. The ablation experiments indicate the effectiveness of each module of the method. In the future, the proposed model or the corresponding modeling idea has the potential to be applied to military rescue, public security investigation and other fields. Once the paper is accepted, we will make the homemade dataset publicly available.