Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography

Zhu, Minghan; Zhang, Songan; Zhong, Yuanxin; Lu, Pingping; Peng, Huei; Lenneman, John K.

doi:10.1109/iros51168.2021.9636384

Cited by 23 publications

(8 citation statements)

References 26 publications

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“…Vehicle detection methods can be categorized into four approaches according to the complexity of expressing the vehicle: bounding box-based [ 42 ], bottom face quadrilateral (BFQ)-based [ 5 ], 3D bounding box-based [ 33 ], and wire frame-based [ 43 ]. Since this paper deals with the BFQ-based approach that provides the vehicle’s position, size, and direction while requiring less computation, the detailed related literature has been reviewed only for this approach.…”

Section: Related Workmentioning

confidence: 99%

“…Surveillance camera-based vehicle detection has been widely used in various intelligent transportation system (ITS) applications [ 1 , 2 ]. In many of these, an accurate vehicle position is required [ 3 , 4 , 5 ]. For instance, autonomous driving based on vehicle-to-infrastructure (V2I) prevents accidents or control vehicles using accurate positions provided by infrastructure surveillance cameras.…”

Section: Introductionmentioning

confidence: 99%

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CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems

Kim

Jung

Suhr

2023

Sensors

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In intelligent transportation systems, it is essential to estimate the vehicle position accurately. To this end, it is preferred to detect vehicles as a bottom face quadrilateral (BFQ) rather than an axis-aligned bounding box. Although there have been some methods for detecting the vehicle BFQ using vehicle-mounted cameras, few studies have been conducted using surveillance cameras. Therefore, this paper conducts a comparative study on various approaches for detecting the vehicle BFQ in surveillance camera environments. Three approaches were selected for comparison, including corner-based, position/size/angle-based, and line-based. For comparison, this paper suggests a way to implement the vehicle BFQ detectors by simply adding extra heads to one of the most widely used real-time object detectors, YOLO. In experiments, it was shown that the vehicle BFQ can be adequately detected by using the suggested implementation, and the three approaches were quantitatively evaluated, compared, and analyzed.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems

Kim

Jung

Suhr

2023

Sensors

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“…In practical applications, the intrinsic and extrinsic parameters of the camera may be unknown. TrafCam3D [39] proposes a robust homography map based on a dual-view network architecture to relieve the IPM distortion.…”

Section: Homograph-based Pv2bevmentioning

confidence: 99%

Vision-Centric BEV Perception: A Survey

Ma¹,

Wang²,

Bai³

et al. 2022

Preprint

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Vision-centric BEV perception has recently received increased attention from both industry and academia due to its inherent merits, including presenting a natural representation of the world and being fusion-friendly. With the rapid development of deep learning, numerous methods have been proposed to address the vision-centric BEV perception. However, there is no recent survey for this novel and growing research field. To stimulate its future research, this paper presents a comprehensive survey of recent progress of vision-centric BEV perception and its extensions. It collects and organizes the recent knowledge, and gives a systematic review and summary of commonly used algorithms. It also provides in-depth analyses and comparative results on several BEV perception tasks, facilitating the comparisons of future works and inspiring future research directions. Moreover, empirical implementation details are also discussed and shown to benefit the development of related algorithms.

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“…To solve this issue, Soochor et al [80] created the BrnoCompSpeed dataset that contains ground-truth speed for performance comparison. This dataset has been employed in several studies [13,35,49,90,119,120] and has almost become a standard dataset for speed measurement performance evaluation.…”

Section: Speed Measurementmentioning

confidence: 99%

Monocular Visual Traffic Surveillance: A Review

Zhang

Feng

Angeloudis

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.

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Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography

Cited by 23 publications

References 26 publications

CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems

CNN-Based Vehicle Bottom Face Quadrilateral Detection Using Surveillance Cameras for Intelligent Transportation Systems

Vision-Centric BEV Perception: A Survey

Monocular Visual Traffic Surveillance: A Review

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