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E-reID: An e-bike re-identification system based on multi-object instance segmentation and retrieval

Kaixuan Cong,Yifan Wang,Jing Yang,Zi Yang,Longyan Wang

Published 2024 in 2024 IEEE Intelligent Vehicles Symposium (IV)

ABSTRACT

Applying existing vehicle re-identification methods directly to the re-identification task of E-bikes comes with high costs for capturing and annotating a specific dataset, and it is prone to missing small E-bikes in dense street scenes. In this paper, an innovative E-bikes re-identification system (E-reID) is proposed to address the challenge of E-bikes re-identification for dense small packed object in complex street scenes with only need for a small detection dataset of E-bikes. This system decomposes the task of re-identification for small E-bikes in complex backgrounds into two sub-tasks: instance segmentation and instance retrieval. The instance segmentation is composed of a specific object detection branch that trained with the custom detection dataset to avoid missing the small E-bikes and a MASK branch trained with publicly available datasets containing similar objects such as motorcars and bicycles. For the instance retrieval task, this paper tested methods such as SIFT matching and HSV histogram for matching the same E-bike in different scenarios. The E-reID system built in this paper demonstrates good performance in the custom re-identification dataset of E-bikes. This paper provides an effective and cost-efficient solution to the re-identification of small-target E-bikes in complex scenes.

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