Real-Time Person Tracking and Association on Doorbell Cameras

摘要

This paper presents key techniques for real-time, multi-person tracking and association on doorbell surveillance cameras at the edge. The challenges for this task are: significant person size changes during tracking caused by person approaching or departing from the doorbell camera, person occlusions due to limited camera field and occluding objects in the camera view, and the requirement for a lightweight algorithm that can run in real time on the doorbell camera at the edge. To address these challenges, we propose a multi-person tracker that uses a detect-track-associate strategy to achieve good performance in speed and accuracy. The person detector only runs at every n-th frame, and between person detection frames a low-cost point-based tracker is used to track the subjects. To maintain subject tracking accuracy, at each person detection frame, a person association algorithm is used to associate persons detected in the current frame to the current and recently tracked subjects and identify any new subjects. To improve the performance of the point-based tracker, human-shaped masks are used to filter out background points. Further, to address the challenge of drastic target scale change during the tracking we introduced an adaptive image resizing strategy to dynamically adjust the tracker input image size to allow the point-based tracker to operate at the optimal image resolution given a fixed number of feature points. For fast and accurate person association, we introduced the Sped-Up LOMO, a fast version of the popular local maximal occurrence (LOMO) person descriptor. The experimental results on doorbell surveillance videos illustrate the efficacy of the proposed person tracking and association framework.