CamLoc: Pedestrian Location Estimation through Body Pose Estimation on Smart Cameras

Advances in hardware and algorithms are driving the exponential growth of Internet of Things (IoT), with increasingly more pervasive computations being performed near the data generation sources. With this wave of technology, a range of intelligent devices can perform local inferences (activity recognition, fitness monitoring, etc.), which have obvious advantages: reduced inference latency for interactive (real-time) applications and better data privacy by processing user data locally. Video processing can benefit many applications and data labelling systems, although performing this efficiently at the edge of the Internet is not trivial. In this paper, we show that accurate pedestrian location estimation is achievable using deep neural networks on fixed cameras with limited computing resources. Our approach, CamLoc, uses pose estimation from key body points detection to extend pedestrian skeleton when the entire body is not in view (occluded by obstacles or partially outside the frame). Our evaluation dataset contains over 2100 frames from surveillance cameras (including two cameras simultaneously pointing at the same scene from different angles), in 42 different scenarios of activity and occlusion. We make this dataset available together with annotations indicating the exact 2D position of person in frame as ground-truth information. CamLoc achieves good location estimation accuracy in these complex scenarios with high levels of occlusion, matching the performance of state-of-the-art solutions, but using less computing resources and attaining a higher inference throughput.