Scalable and parameterized dynamic time warping architecture for efficient vehicle re-identification

Deng, G.Y.; Zhou, Hanqing; Yu, Guangyu; Yan, Zeyu; Hu, Yu; Xu, Xiaowei

doi:10.1109/ictis.2017.8047741

Cited by 2 publications

(1 citation statement)

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“…Therefore, researchers proposed methods Wang et al (2014) Ramachandran et al (2002 for V-reID by combining image/video processing techniques with sensor data. Deng et al (2017) proposed a V-reID method based on dynamic time warping Müller (2007) and magnetic signature. Wang et al (2014) proposed a vehicle reidentification system with self-adaptive time windows to estimate the mean travel time for each time period on the freeway under traffic demand and supply uncertainty.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A survey of advances in vision-based vehicle re-identification

Khan

Ullah

2019

Computer Vision and Image Understanding

126

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Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.

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Section: Hybrid Methodsmentioning

confidence: 99%