Scalable Object Tracking in Smart Cities
Scalable Object Tracking in Smart Cities
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DOI：
10.1109/bigdata47090.2019.9005472
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Jose Stovall;Austin Harris;Amanda O'Grady;Mina Sartipi
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文献类型：
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作者：
Jose Stovall;Austin Harris;Amanda O'Grady;Mina Sartipi
在配备摄像头的智能城市中，一个理想的用例是检测和跟踪物体。虽然已经使用各种方法实现了对象检测，但对象跟踪提出了不同的问题;跟踪对象需要在视频的每个帧之间建立对象持久性。虽然已经提出了许多技术作为问题的解决方案，但考虑到可扩展性的实现尚未开发，并提出了许多新的挑战。本文提出了e-SORT，可扩展的对象跟踪的解决方案，使用简单的在线和实时跟踪（SORT）算法的增强版本。除了其可扩展性之外，e-SORT还存储每个对象位置的映射，以便每个对象的完整路径可用，并且可以计算多个度量（例如速度和加速度）。电子SORT的能力和我们提出的解决方案，可扩展的对象跟踪测试和评估查塔努加田纳西州的生活城市测试平台。
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.