Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Espinosa, Jorge E.; Velastín, Sergio A.; Branch, John W.

doi:10.1109/tits.2020.2997084

Cited by 29 publications

(14 citation statements)

References 98 publications

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“…Menurut (Espinosa, Velastin, and Branch 2021) dalam penelitiannya yang berjudul "Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review" Menjelaskan ukuran kinerja yang umumnya digunakan, kumpulan data yang tersedia untuk umum (memperkenalkan Kumpulan Data Sepeda Motor Perkotaan dengan hasil evaluasi kuantitatif sebesar 10% untuk berbagai detektor), membahas tantangan ke depan dan menyajikan serangkaian kesimpulan dengan pekerjaan masa depan yang diusulkan di area yang berkembang.…”

Section: Pendahuluanunclassified

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Mulyana¹,

Rofik²

2022

jptam

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Negara Indonesia mempunyai kepadatan penduduk yang sangat padat, terutama dikota-kota besar yang dimana jalan selalu dipadati oleh berbagai jenis kendaran. Pada jam sibuk banyaknya kendaraan yang membuat kemacetan dijalan. Oleh karna itu dibutuhkan pembangunan pelebaran jalan untuk menampung kendaraan yang dipadati oleh berbagai jenis kendaraan yang melintas. Agar pembangunan pelebaran jalan yang tepat pada lokasi yang sering terjadinya kepadatan, maka dibutuhkan sistem pendeteksian jenis-jenis kendaraan yang melintas dijalan raya. Meningkatnya pada macam-macam penelitian tentang pengolahan citra digital diantaranya tentang pendeteksian objek, untuk klasifikasi deteksi jenis kendaraan dijalan raya. Pada penelitian ini penulis membuat sistem pendedeteksi objek memakai metode YOLOV5 untuk mendeteksi jenis kendaraan dijalan raya. Penulis menggunakan dataset sebesar 1332 gambar dengan kelas bajaj, becak, bus, mobil, mobil molen, mobil pik’up, sepedah, sepeda motor, dan truk. Pada hasil penelitian menggunakan metode YOLOV5 yang dapat mengenali objek secara konsisten dengan tingkat akurasi yang cukup tinggi dan memiliki nilai akurasi 90%.

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Section: Pendahuluanunclassified

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Mulyana¹,

Rofik²

2022

jptam

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show abstract

“…As far as we know, although many motorcycle helmet detection methods have been proposed, the datasets used are small and not comprehensive. In the currently published dataset, the UMD dataset [18] is only collected in one place, there is no diversity; OSF dataset [51] is obtained from 12 observation points of seven cities in Myanmar using cameras, which lack night images, and the resolution is 1920 × 1080, which is not obtained in traffic monitoring scenes. In view of the lack of available data for the actual traffic scene, we have built a large‐scale dataset, named HFUT‐MH.…”

Section: Hfut‐mh Datasetmentioning

confidence: 99%

“…The first step in the helmet detection of motorcyclists is usually motorcycle detection. In [18], the research status of motorcycle detection based on traditional methods and deep learning is reviewed in detail. Currently, some methods based on video or image have been proposed for helmet detection.…”

Section: Related Workmentioning

confidence: 99%

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Jia

Liang

et al. 2021

IET Image Processing

117

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In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft-NMS instead of NMS. A new motorcycle helmet dataset (HFUT-MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state-of-the-art methods. Our method achieves mAP of 97.7%, F1-score of 92.7% and frames per second (FPS) of 63, which outperforms other state-of-the-art detection methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Many notable surveys related to change detection have been published, as summarized in Table I. These include many excellent surveys of the traditional background subtraction methods [36], [37], [42], [45], [47], deep neural network methods for background subtraction [48], traffic monitoring [38], [50], background initialization [43], foreground detection [40], wide area motion detection [39], maritime surveillance [41], [44], and moving object detection with a moving camera [46], [49].…”

Section: Comparison With Previous Reviewsmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and realtime processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.

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Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Cited by 29 publications

References 98 publications

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

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