Vehicle Detection and Tracking in Real-time using YOLOv4-tiny

Amrouche, Abderrahmane; Bentrcia, Youssouf; Abed, Abir El; Hezil, Nabil

doi:10.1109/ispa54004.2022.9786330

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Binocular vision is a kind of stereoscopic vision that simulates real vision to explain complex three-dimensional structures and spatial relationships and can provide depth and spatial information [23,24]. Binocular vision technology can help human beings to complete some detection and recognition tasks more accurately, conveniently and in real time when their eyes cannot judge accurately.…”

Section: Binocular Vision Positioning Technologymentioning

confidence: 99%

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Zhang,

Gong,

Chen

2024

WEVJ

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Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.

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Section: Binocular Vision Positioning Technologymentioning

confidence: 99%

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Zhang,

Gong,

Chen

2024

WEVJ

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“…Rajput et al [ 112 ] proposed a toll management system, using Yolov3 architecture, for vehicle identification and classification. Amrouche and his colleagues proposed a Yolov4 architecture for a real-time vehicle detection and tracking system [ 113 ]. Wang et al [ 114 ] introduced an integrated part-aware refinement network, which combines multi-scale training and component confidence generation strategies in vehicle detection.…”

Section: Application Of Dcnn For Vehicle Detection and Classificationmentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

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“…Deep learning-based methods have become the main trend in the first stage, image object detection, since they can be utilized in small and large networks while preserving speed and accuracy. One of the most common deep learning-based detection algorithms is YOLO ( 21 ) and its related algorithms such as YOLO-LITE ( 22 ) for non-GPU computers, Tinier-YOLO ( 23 ), YOLOv4-tiny ( 24 ) for real-time applications. Furthermore, Ouyang et al ( 25 ) propose a JointDeep method for identifying candidate parts based on the previous detection method, deformable part models ( 26 ).…”

Section: Related Workmentioning

confidence: 99%

Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

Nguyen

Sartipi

et al. 2023

Transportation Research Record: Journal of the Transportation R

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An essential application in intelligent transportation systems is multi-target multi-camera tracking (MTMCT), where the target’s activity is tracked from different cameras. Although the tracking-by-detection scheme is the primary paradigm in MTMCT, the object association information from the video frames is lost. This is mainly because the multi-camera multi-object matching uses the information from the video frames separately. To solve this problem and leverage this association information, we propose an MTMCT framework, where features are built in the form of a graph and a graph similarity algorithm is used to match multi-camera objects. In this paper, we focus on the real-time scenario, where only the past images are used to match an object. Our method achieves an IDF1 score (the ratio of the number of correctly identified objects to the number of ground truth and average objects) of 0.75 with a rate of 14 frames per second (fps).

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Vehicle Detection and Tracking in Real-time using YOLOv4-tiny

Cited by 10 publications

References 21 publications

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

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