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Simple online and realtime tracking people with new “soft-iou” metric
Author(s): Sergey Menshov; Yan Wang; Andrey Zhdanov; Eugene Varlamov; Dmitry Zhdanov
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Paper Abstract

Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation1 and autonomous driving2. In tracking by-detection, a major challenge of online MOT is how to robustly associate noisy object detections on a new video frame with previously tracked objects. This paper aims to build technology that can track a movement of people via surveillance cameras that are located in stores, but not only (theoretically, the algorithm may be applicable to the location of the camera at any premises). The algorithm works with a variety of camera angles that allows. The main innovation of the paper is that algorithm SORT has been updated to consider the difference between datasets used on competitions and the real ones. The difference is that recognition is not perfect in data created by the program. People’s contours may be of different size (rectangles corresponding to the same man may differ twice) and some of them may be not recognized. The new metric of proximity called “soft-iou” has been introduced in SORT. We have achieved the accuracy of 95% for the daily number of visitors for one of jewelry retail chains. This level of accuracy allows applying the algorithm in different areas: not only retail stores, but also shopping centers, sports events, performances, traffic in public transport, etc.

Paper Details

Date Published: 18 December 2019
PDF: 7 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420M (18 December 2019); doi: 10.1117/12.2547922
Show Author Affiliations
Sergey Menshov, ITMO Univ. (Russian Federation)
Yan Wang, ITMO Univ. (Russian Federation)
Andrey Zhdanov, ITMO Univ. (Russian Federation)
Eugene Varlamov, ITMO Univ. (Russian Federation)
Dmitry Zhdanov, ITMO Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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