A vehicle re-identification algorithm based on multi-sensor correlation

Tian, Yu; Dong, Hui; Jia, Limin; Li, Si-yu

doi:10.1631/jzus.c1300291

Cited by 17 publications

(10 citation statements)

References 16 publications

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“…In the initial vehicle matching problem, sensor tag matching is adopted. Tian et al [17] proposed an algorithm for vehicle Re-ID based on multiple sensor nodes. According to the matching results of the same vehicle label obtained by different nodes, the vehicle state was determined and the label segmentation was modified.…”

Section: Vehicle Re-id Based On Artificial Design Featurementioning

confidence: 99%

Object Re-Identification Based on Deep Learning

Li¹,

Zhou²

2019

Visual Object Tracking With Deep Neural Networks

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With the explosive growth of video data and the rapid development of computer vision technology, more and more relevant technologies are applied in our real life, one of which is object re-identification (Re-ID) technology. Object Re-ID is currently concentrated in the field of person Re-ID and vehicle Re-ID, which is mainly used to realize the cross-vision tracking of person/vehicle and trajectory prediction. This chapter combines theory and practice to explain why the deep network can reidentify the object. To introduce the main technical route of object Re-ID, the examples of person/vehicle Re-ID are given, and the improvement points of existing object Re-ID research are described separately.

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Section: Vehicle Re-id Based On Artificial Design Featurementioning

confidence: 99%

Object Re-Identification Based on Deep Learning

Li¹,

Zhou²

2019

Visual Object Tracking With Deep Neural Networks

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“…In [8], the experiment system located at Jiao Da Dong Lu, Haidian District, Beijing, a two-way road with a single lane on each side. The experiment is conducted on a section of 226 m long road heading from south to north.…”

Section: A Vehicle Re-identification Algorithm Based On Multi-sensor Correlationmentioning

confidence: 99%

“…There are about 19368 practically measured vehicles in the data. Based on these data, the vehicle re-identification can effectively improve the accuracy of vehicle identification, especially for the buses [8].…”

Section: A Vehicle Re-identification Algorithm Based On Multi-sensor Correlationmentioning

confidence: 99%

“…Nowadays, object re-identification (Re-ID) has been more and more attractive in computer vision, which is due to the requirements of intelligent surveillance in different occasions. Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video [1][2] [3][4] [5], and automatic vehicle identification supports automatic operation of Intelligent Transportation Systems (ITS) by providing various data, such as real-time travel time information, headway preference of drivers, real-time crash prediction, signalized intersection delays [6][7] [8]. Therefore, more and more researchers try to find a good way to realize the object re-identification application including person Re-ID and vehicle Re-ID.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers focus on discussing how to provide vehicle signatures with higher accuracy for vehicle identification algorithms. In [8], others focus on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box [9]. There are also some researchers devoting themselves to a Bayesian framework in order to tackle the vehicle re-identification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Metric Learning for Object Re-identification in Intelligent Surveillance

Yi¹

2017

dtetr

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In recent years, object re-identification has been a significant topic in computer vision, which is expected in the application of artificial intelligent surveillance. To tackle the problem, metric learning has become an optimal method, and the approaches are concerned with learning a distance function tuned to a particular identification task, and have been shown to be useful in conjunction with nearest-neighbor methods and other techniques. This paper provides a survey of existing metric learning approaches for object re-identification, which focuses on the methods based on the application of metric learning. The detailed description of methods is presented for comparison, and several related main datasets are briefly introduced. In this paper, all papers are published ranging from 2013 to 2016, which provides an overview of progress in this area.

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A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions

Ghosh

2021

Algorithms for Intelligent Systems

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A vehicle re-identification algorithm based on multi-sensor correlation

Cited by 17 publications

References 16 publications

Object Re-Identification Based on Deep Learning

Object Re-Identification Based on Deep Learning

A Survey on Metric Learning for Object Re-identification in Intelligent Surveillance

A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions

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