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Smart city article details
| Title | Scalable Object Tracking In Smart Cities |
|---|---|
| ID_Doc | 47336 |
| Authors | Stovall J.; Harris A.; O'Grady A.; Sartipi M. |
| Year | 2019 |
| Published | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
| DOI | http://dx.doi.org/10.1109/BigData47090.2019.9005472 |
| Abstract | 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. © 2019 IEEE. |
| Author Keywords | Machine Learning; Object Detection; Object Tracking; Smart City; Smart Infrastructure |
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