Real-Time Detection of Traffic Anomalies Near Roundabouts

Pramanik, Anima; Sarkar, Sobhan; Djeddi, Chawki; Maiti, J.

doi:10.1007/978-3-031-04112-9_19

“…Background Models Challenge Dataset [38] iLids Parked Vehicle Detection Dataset [27] CDnet dataset 2014 [39] Rapid vehicle detection Wang et al [40] 2019 2019 AI City Challenge [41] ROI determination and anomaly detection Premachandra et al [42] 2019 Intersection (Japan) Vehicle detection in 360-degree view Zhang et al [43] 2020 CDnet dataset 2014 [39] Vehicle detection under varying illumination Zeng et al [44] 2020 CDnet dataset 2014 [39] Vehicle detection in low visibility and dynamic background Shi et al [45] 2021 Highway traffic (New Jersey) Anomaly detection Liu et al [46] 2021 Highway traffic (New Jersey) Traffic monitoring using an edge-cloud solution Thomas et al [47] 2021 GRAM dataset [31] V5 dataset [48] Vehicle detection Alhuthali et al [49] 2022 Road traffic (Makkah) Vehicle detection and counting Jaiswal and Kumar [50] 2022 Highway traffic (India) Wrong-Way vehicle detection on edge device Ghahremannezhad et al [51] 2022 Road traffic (New Jersey) ATON dataset [52] Vehicle detection with shadow removal Pramanik et al [53] 2022 YouTube-8M dataset [54] Anomaly detection in roundabouts cameras remotely and direct the attention to a specific event or survey a different area. There are several studies, especially in recent years, that address motion segmentation in the case of a moving camera [57].…”

Section: Refmentioning

confidence: 99%

Object Detection in Traffic Videos: A Survey

Ghahremannezhad

¹

,

Shi²,

Liu

³

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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“…Background Models Challenge Dataset [38] iLids Parked Vehicle Detection Dataset [27] CDnet dataset 2014 [39] Rapid vehicle detection Wang et al [40] 2019 2019 AI City Challenge [41] ROI determination and anomaly detection Premachandra et al [42] 2019 Intersection (Japan) Vehicle detection in 360-degree view Zhang et al [43] 2020 CDnet dataset 2014 [39] Vehicle detection under varying illumination Zeng et al [44] 2020 CDnet dataset 2014 [39] Vehicle detection in low visibility and dynamic background Shi et al [45] 2021 Highway traffic (New Jersey) Anomaly detection Liu et al [46] 2021 Highway traffic (New Jersey) Traffic monitoring using an edge-cloud solution Thomas et al [47] 2021 GRAM dataset [31] V5 dataset [48] Vehicle detection Alhuthali et al [49] 2022 Road traffic (Makkah) Vehicle detection and counting Jaiswal and Kumar [50] 2022 Highway traffic (India) Wrong-Way vehicle detection on edge device Ghahremannezhad et al [51] 2022 Road traffic (New Jersey) ATON dataset [52] Vehicle detection with shadow removal Pramanik et al [53] 2022 YouTube-8M dataset [54] Anomaly detection in roundabouts cameras remotely and direct the attention to a specific event or survey a different area. There are several studies, especially in recent years, that address motion segmentation in the case of a moving camera [57].…”

Section: Refmentioning

confidence: 99%

Object Detection in Traffic Videos: A Survey

Ghahremannezhad¹,

Shi²,

liu³

2022

Preprint

0

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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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