PCTrack: Accurate Object Tracking for Live Video Analytics on Resource-Constrained Edge Devices

Xinyi Zhang, Haoran Xu, Chenyun Yu, Guang Tan

Abstract: The task of live video analytics relies on real-time object tracking that typically involves computationally expensive deep neural network (DNN) models. In practice, it has become essential to process video data on edge devices deployed near the cameras. However, these edge devices often have very limited computing resources and thus suffer from poor tracking accuracy. Through a measurement study, we identify three major factors contributing to the performance issue: outdated detection results, tracking error accumulation, and ignorance of new objects. We introduce a novel approach, called Predict & Correct based Tracking, or PCTrack, to systematically address these problems. Our design incorporates three innovative components: 1) a Predictive Detection Propagator that rapidly updates outdated object bounding boxes to match the current frame through a lightweight prediction model; 2) a Frame Difference Corrector that refines the object bounding boxes based on frame difference information; and 3) a New Object Detector that efficiently discovers newly appearing objects during tracking. Experimental results show that our approach achieves remarkable accuracy improvements, ranging from 19.4% to 34.7%, across diverse traffic scenarios, compared to state of the art methods.

External IDs:dblp:journals/tcsv/ZhangXYT25