A vehicle re-identification framework based on the improved multi-branch feature fusion network

Rong, Leilei; Xu, Yan; Zhou, Xiaolei; Han, Lisu; Li, Linghui; Pan, Xuguang

doi:10.1038/s41598-021-99646-6

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…Using the driver's visual field image data and the corresponding vehicle driving state data (steering wheel angle and vehicle speed) to reverse the driving behavior habits during driving is the focus of our research. The wide application of neural networks in various fields provides support for our work 1 , 2 .…”

Section: Introductionsupporting

confidence: 54%

Research on imaging method of driver's attention area based on deep neural network

Zhao

et al. 2022

Sci Rep

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In the driving process, the driver's visual attention area is of great significance to the research of intelligent driving decision-making behavior and the dynamic research of driving behavior. Traditional driver intention recognition has problems such as large contact interference with wearing equipment, the high false detection rate for drivers wearing glasses and strong light, and unclear extraction of the field of view. We use the driver's field of vision image taken by the dash cam and the corresponding vehicle driving state data (steering wheel angle and vehicle speed). Combined with the interpretability method of the deep neural network, a method of imaging the driver's attention area is proposed. The basic idea of this method is to perform attention imaging analysis on the neural network virtual driver based on the vehicle driving state data, and then infer the visual attention area of the human driver. The results show that this method can realize the reverse reasoning of the driver's intention behavior during driving, image the driver's visual attention area, and provide a theoretical basis for the dynamic analysis of the driver's driving behavior and the further development of traffic safety analysis.

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Section: Introductionsupporting

confidence: 54%

Research on imaging method of driver's attention area based on deep neural network

Zhao

et al. 2022

Sci Rep

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“…But the limitation are, it does not work on severe climatic conditions [ 44 , 45 ]. Using classifier like Naïve Bayes, vehicle faults [ 46 ] are identified with the features of temperature, noise and vibration of the vehicles even parking management system [ 47 ] are performed. Similarly using KNN classifier, from 4 type of scenario, vehicle classified with the help of forward scattering radars and gain accuracy of 99% [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

Application of an ensemble CatBoost model over complex dataset for vehicle classification

M.,

R. K.,

et al. 2024

PLoS ONE

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The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

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“…Wang et al [123] segmented the image vertically and horizontally to extract features, which improves the accuracy of vehicle re-recognition. Rong et al [124] fuse local-global features to obtain more vehicle information and enhance the learning ability of vehicle recognition. Yang et al [125] studied the two-branch network based on pyramid feature learning.…”

Section: Vehicle Re-identification Based On Local Featurementioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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A vehicle re-identification framework based on the improved multi-branch feature fusion network

Cited by 13 publications

References 34 publications

Research on imaging method of driver's attention area based on deep neural network

Research on imaging method of driver's attention area based on deep neural network

Application of an ensemble CatBoost model over complex dataset for vehicle classification

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

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