Vehicle Re-Identification With Image Processing and Car-Following Model Using Multiple Surveillance Cameras From Urban Arterials

Xiong, Zhongxia; Li, Ming; Ma, Yalong; Wu, Xinkai

doi:10.1109/tits.2020.3006047

Cited by 20 publications

(9 citation statements)

References 25 publications

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“…To mark the spatiotemporal and speed information of a vehicle, we must transform its spatial coordinates. Perspective transformation is used to transform the vehicle driving area under the camera vision to a fixed-size rectangle [ 11 ], as shown in Figure 6 .…”

Section: Methodsmentioning

confidence: 99%

“…Each vehicle is attached by the camera mileage and the picture shooting time. According to the spatial coordinate transformation method [ 11 ], we infer the spatial positions of vehicles in tunnel from the perspective of camera monitoring and obtain their temporal information by comparing timestamps of monitoring cameras. We use the vehicle ReID to determine whether the HAZMAT vehicles are exiting the tunnel within a normal time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Jia

Wang

et al. 2023

Computational Intelligence and Neuroscience

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Vehicles transporting hazardous material (HAZMAT) pose a severe threat to highway safety, especially in road tunnels. Vehicle reidentification is essential for identifying and warning abnormal states of HAZMAT vehicles in road tunnels. However, there is still no public dataset for benchmarking this task. To this end, this work releases a real-world tunnel HAZMAT vehicle reidentification dataset, VisInt-THV-ReID, including 10,048 images with 865 HAZMAT vehicles and their spatiotemporal information. A method based on multimodal information fusion is proposed to realize vehicle reidentification by fusing vehicle appearance and spatiotemporal information. We design a spatiotemporal similarity determination method for vehicles based on the spatiotemporal law of vehicles in tunnels. Compared with other reidentification methods based on multimodal information fusion, i.e., PROVID, Visual + ST, and Siamese-CNN, experimental results show that our approach significantly improves the vehicle reidentification recognition precision.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Jia

Wang

et al. 2023

Computational Intelligence and Neuroscience

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show abstract

“…The model combines the desired speed, acceleration performance, following distance, and driver’s habit during vehicle driving, and is a commonly used following model in various simulators, such as SUMO. The IDM is also commonly used in CAV-related fields as a common driving model [ 71 , 72 , 73 ].…”

Section: Dynamic Scenario Generationmentioning

confidence: 99%

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

Zhu¹,

Wang²,

Meng³

et al. 2022

Sensors

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The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized.

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“…However, the vehicle matching task is rarely investigated independently, and the most relevant task, vehicle re‐identification, has received increasing attention because of the promising performance and convenience of the application. Therefore, many researchers (Chu et al, 2019; Guo et al, 2019; Rong et al, 2021; Wang, Peng, et al, 2020; Xiong et al, 2020; Zhou et al, 2018) have mainly focused on this area of study, and these methods are extremely useful in vehicle matching research.…”

Section: Introductionmentioning

confidence: 99%

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Qin

Zhang

et al. 2022

Expert Systems

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There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

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Vehicle Re-Identification With Image Processing and Car-Following Model Using Multiple Surveillance Cameras From Urban Arterials

Cited by 20 publications

References 25 publications

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

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