Scalable Object Tracking in Smart Cities
Jose Stovall, Austin Harris, Amanda O'Grady, Mina Sartipi · 2019
In smart cities equipped with cameras, one desirable use-case is to detect and track objects. While object detection has been implemented using various methods, object tracking poses a different problem; to track an object requires object permanence to be established between each frame of video. While many technologies have been proposed as a solution for problem, an implementation with scalability in mind has not been developed and poses many new challenges. This paper proposes e-SORT, a solution for scalable object tracking using an enhanced version of the Simple Online and Realtime Tracking (SORT) algorithm. Beyond its scalability, e-SORT stores a mapping of each objects' locations such that the full path of each object is available and several metrics (such as velocity and acceleration) can be calculated. Both e-SORT's abilities and our proposed solution to scalable object tracking are tested and evaluated on Chattanooga Tennessee's live urban testbed.