Fast extraction of traffic parameters and reidentification of vehicles from video data

Woesler, Richard

doi:10.1109/itsc.2003.1252055

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…Many researchers considered appearance descriptors Woesler (2003) In these methods, discriminatory information is extracted from the query vehicle image. Woesler (2003) extracted 3d vehicle models and color information from the top plane of vehicle for V-reID. Shan et al (2005) proposed a feature vector which is composed of edge-map distances between a query vehicle image and images of other vehicles within the same camera view.…”

Section: Hand-crafted Feature Based Methodsmentioning

confidence: 99%

“…Sample images of different vehicles are presented from PKU-VD dataset.are recently published methods in both categories. The 8 handcrafted feature based methods are: the 3d and color information (3DCI)Woesler (2003), the edge-map distances (EMD)Shan et al (2005), the 3d and piecewise model (3DPM)Guo et al (2008), the 3d pose and illumination model (3DPIM)Hou et al (2009), the attribute based model (ABM)Feris et al (2012), the multi-pose model (MPM)Zheng et al (2015), the bounding box model (BBM)Zapletal and (2016), and the license number plate (LNP)Watchar and (2017). The 12 deep feature methods are: the progressive vehicle re-identification (PROVID)Liu et al (2016c), the deep relative distance learning (DRDL)Liu et al (2016a), the deep color and texture (DCT)Liu et al (2016b), the orientation invariant model (OIM)Wang et al (2017), the visual spatio-temporal model (VSTM)Shen et al (2017), the cross-level vehicle recognition (CLVR)Kanacı et al (2017), the triplet-wise training (TWT)Zhang et al (2017), the feature fusing model (FFM)Tang et al (2017), the deep joint discriminative learning (DJDL)Li et al (2017b), the Null space based Fusion of Color and Attribute feature (NuFACT)Liu et al (2018), the multi-view feature (MVF)Zhou et al (2018), and the group sensitive triplet embedding (GSTE)Bai et al (2018).…”

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confidence: 99%

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

Khan

Ullah

2019

Computer Vision and Image Understanding

137

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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: Hand-crafted Feature Based Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Khan

Ullah

2019

Computer Vision and Image Understanding

137

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“…In the early research on computer vision, Liu et al [20] proposed a method to re-identify vehicles based on 3D vehicle models and the extraction of the color of the vehicle's top surface, which corrected the shadows and light reflections on wet streets and achieved high recognition accuracy. Woesler [21] used linear regression, color histogram, and directional gradient histogram to solve the reidentification problem. In the study of re-identification, there are methods based on global features [22,23] and local features [24,25] in apparent features.…”

Section: Vehicle Re-identification Based On Traditional Featuresmentioning

confidence: 99%

Vehicle Re-Identification System Based on Appearance Features

Yang

Zhu

et al. 2022

Security and Communication Networks

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Aiming at the low recognition accuracy caused by the problems of angle, illumination, and occlusion in vehicle re-identification based on deep learning, a vehicle re-identification method based on multibranch network feature extraction and two-stage retrieval feature is proposed. The multibranch feature extraction module uses ResNet-50 as the backbone network to extract the vehicle’s attribute features and apparent features, respectively, and uses the attribute features for rough retrieval. On this basis, the attribute features and apparent features are fused for fine retrieval. Through experiments, the accuracy of vehicle re-identification on Veri-776 data set and VehicleID datasets is significantly improved. In addition, based on the improved algorithm, this paper designs and develops a vehicle re-identification system, which realizes the functions of inputting file directory, selecting target image, and querying result image, and provides a visual technical scheme for vehicle re-identification and retrieval in the real scene.

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“…In order to minimize the number of cameras used to monitor traffic within a city, Ref. 106 presented a novel strategy for vehicle reidentification, which matches vehicles leaving one monitored region with those entering another one based on color, appearance, and spatial dimensions of the vehicles. Reference 107 presented a prototype of a smart camera with embedded DSP implementations for traffic…”

Section: Traffic Flowmentioning

confidence: 99%

Computer vision in roadway transportation systems: a survey

Loce

Bernal

et al. 2013

J. Electron. Imaging

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Abstract. There is a worldwide effort to apply 21st century intelli-gence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emis-sion reduction. Computer vision is playing a key role in this transpor-tation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges includ-ing imaging under a variety of environmental and illumination condi-tions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.22.4.041121]

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Fast extraction of traffic parameters and reidentification of vehicles from video data

Cited by 12 publications

References 10 publications

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

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

Vehicle Re-Identification System Based on Appearance Features

Computer vision in roadway transportation systems: a survey

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