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Object Tracking for a Smart City Using IoT and Edge Computing

Image Processing Center, BeiHang University, XueYuan Road No. 37, HaiDian District, Beijing 100083, China
College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA
Software and Computational Systems, DATA61, CSIRO E, Level 1, Synergy Building 801, Black Mountain Science and Innovation Park, Clunies Ross Street, Black Mountain, PO Box 1700, Canberra, ACT 2601, Australia
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(9), 1987;
Received: 27 March 2019 / Revised: 20 April 2019 / Accepted: 20 April 2019 / Published: 28 April 2019
(This article belongs to the Special Issue Smart IoT Sensing)
PDF [13110 KB, uploaded 28 April 2019]
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As the Internet-of-Things (IoT) and edge computing have been major paradigms for distributed data collection, communication, and processing, smart city applications in the real world tend to adopt IoT and edge computing broadly. Today, more and more machine learning algorithms would be deployed into front-end sensors, devices, and edge data centres rather than centralised cloud data centres. However, front-end sensors and devices are usually not so capable as those computing units in huge data centres, and for this sake, in practice, engineers choose to compromise for limited capacity of embedded computing and limited memory, e.g., neural network models being pruned to fit embedded devices. Visual object tracking is one of many important elements of a smart city, and in the IoT and edge computing context, high requirements to computing power and memory space severely prevent massive and accurate tracking. In this paper, we report on our contribution to object tracking on lightweight computing including (1) using limited computing capacity and memory space to realise tracking; (2) proposing a new algorithm region proposal correlation filter fitting for most edge devices. Systematic evaluations show that (1) our techniques can fit most IoT devices; (2) our techniques can keep relatively high accuracy; and (3) the generated model size is much less than others. View Full-Text
Keywords: Internet-of-Things; edge computing; smart city; object tracking; lightweight computing Internet-of-Things; edge computing; smart city; object tracking; lightweight computing

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Zhang, H.; Zhang, Z.; Zhang, L.; Yang, Y.; Kang, Q.; Sun, D. Object Tracking for a Smart City Using IoT and Edge Computing. Sensors 2019, 19, 1987.

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