Comparison of Performance of Different Background Subtraction Methods for Detection of Heavy Vehicles

Canayaz, Emre; Böcekçi, Veysel Gökhan

doi:10.23919/spa.2018.8563409

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…NGSIM US-101 highway dataset (USA) [29] GRAM Road-Traffic Monitoring dataset [31] Urban traffic [32] Vehicle detection and counting Hadi et al [33] 2017 CDnet dataset 2012 [25] Vehicle detection and tracking in the presence of shadow and partial occlusion Yaghoobi et al [34] 2018 Road traffic (Madrid/Tehran) Vehicle detection in different weather conditions Cheng et al [35] 2018 Highway traffic Congestion detection in bad weather Canayaz and Veysel [36] 2018 Highway traffic (Turkey) Heavy vehicle detection Garg et al [37] 2019…”

Section: Refmentioning

confidence: 99%

Object Detection in Traffic Videos: A Survey

Ghahremannezhad

Shi²,

Liu

2023

IEEE Trans. Intell. Transport. Syst.

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Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.

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Section: Refmentioning

confidence: 99%

Object Detection in Traffic Videos: A Survey

Ghahremannezhad

Shi²,

Liu

2023

IEEE Trans. Intell. Transport. Syst.

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Section: Refmentioning

confidence: 99%

Object Detection in Traffic Videos: A Survey

Ghahremannezhad¹,

Shi²,

liu³

2022

Preprint

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<p>Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.</p>

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