Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Zhiyong Yu,Lei Han,Qi An,Huihui Chen,Houchun Yin,Zhiwen Yu

doi:10.1109/access.2020.2979933

Abstract

Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs.

Highlights

Target tracking is very important in public safety management
Since the coverage area of video surveillance system is limited, and the coverage by casual witnesses are not stable, a natural idea is building a more available tracking system that contains the stationary cameras, and the mobile phones carried by ordinary citizens [7]
We develop the Co-Tracking system that illustrates the idea of collaborative sensing, and make further contributions as following: 1. We propose a location prediction algorithm named Middle Query Location Prediction (MQLP) to utilize intermediate query and large-scale crowdsourced objects’ trajectories to predict the location of a moving object after a specific time interval

Summary

Introduction

Target tracking is very important in public safety management. Once a hit-and-run or child abduction happens, the police usually searches the suspect from records of the pre-deployed video surveillance system [1,2,3] and reports from casual witnesses as in AMBER Alert. Since the coverage area of video surveillance system is limited, and the coverage by casual witnesses are not stable, a natural idea is building a more available tracking system that contains the stationary cameras, and the mobile phones carried by ordinary citizens [7]. We call this idea as “collaborative sensing”. To track a target via collaborative sensing of stationary cameras and mobile phones, some challenges need to be addressed. Within the predicted location area, we need schedule stationary cameras and mobile phones to execute tracking

Objectives

Results

Conclusion