Multipedestrian Online Tracking Based on Social Force-Predicted Deformable Key-Points Mapping via Compressive Sensing

Multipedestrian Online Tracking and positioning are the research hotspots and difficulties in the field of computer vision. In this article, we propose a multipedestrian online tracking method based on social force-predicted deformable key-points mapping and compressive sensing. First, part affinity fields are utilized to detect body key-points of pedestrians, and then rectangle boundaries of moving pedestrians can be constrained based on these key-points, the outer rectangle of the key-points replacing the conventional bounding box. Since pedestrian tracking is vulnerable to occlusion and dynamic deformation, a deformable convolution network is applied to extract delicate features. There is one more point, based on the theory of compressive sensing, a very sparse measurement matrix is designed to efficiently compress the features for the pedestrians, which maps features from high-dimensional space to low-dimensional space and preserves the structure of the image feature space of pedestrians. Compressed features are used to measure the similarity between pedestrians in adjacent frames. Furthermore, a pedestrian social force model is adopted, which can predict the trajectory of pedestrians to deal with the problems of partial occlusion and similar appearance, meanwhile making the estimated location more accurately. Finally, extensive experiments on the MOT16 benchmark demonstrate the superior performance of the proposed tracking method. The main tracking performance parameters are significantly improved, with multiobject tracking accuracy and multiobject tracking precision reaching 62.8% and 81.2%, respectively.