Vision‐based vehicle behaviour analysis: a structured learning approach via convolutional neural networks

Mou, Luntian; Xie, Hualong; Mao, Shasha; Zhao, Pengfei; Chen, Yanyan

doi:10.1049/iet-its.2019.0419

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Furthermore, one of the most predominant segments of AVS, traffic scene analysis, was covered to understand scenes from a challenging and crowded movable environment [102], improve performance by making more expensive spatial-feature risk prediction [112] and on-road damage detection [120]. For this purpose, HRNet + contrastive loss [104], Multi-Stage Deep CNN [106], 2D-LSTM with RNN [108], DNN with Hadamard layer [110], Spatial CNN [112], OP-DNN [113] and the methods mentioned in Table 9 were reviewed. However, there are still some limitations, for instance, data dependency or relying on pre-labelled data, decreased accuracy in challenging traffic or at nighttime.…”

Section: Discussionmentioning

confidence: 99%

“…Mou et al proposed a vision-based vehicle behavior prediction system by incorporating vehicle behavior structural information into the learning process, obtaining a discrete numerical label from the detected vehicle [113]. The OPDNN (overfitting-preventing DNN) was constructed using the structured label as final prediction architecture, and after more than 7000 iterations, 44.18% more accuracy on-road vehicle action than CNN was achieved.…”

Section: High Interruption Formentioning

confidence: 99%

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Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems.

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Section: Discussionmentioning

confidence: 99%

Section: High Interruption Formentioning

confidence: 99%

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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“…of neurons of output-layer, y l−1 m is the m characteristic pattern of layer l-1, and w l m,n is the connected weights. Complexity performance on large-scale datasets [38][39][40][41]. To choose the suitable CNN model, initially, we fine-tuned the existing stateof-the-art AlexNet, Inception-v3, GoogleNet, VGG, and ResNet models according to the classes of the collected dataset.…”

Section: Fully Connected Layermentioning

confidence: 99%

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

et al. 2021

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In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.

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Interaction recognition and intervention based on context feature fusion of learning behaviors in interactive learning environments

Xia

2021

Interactive Learning Environments

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Vision‐based vehicle behaviour analysis: a structured learning approach via convolutional neural networks

Cited by 6 publications

References 24 publications

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

Interaction recognition and intervention based on context feature fusion of learning behaviors in interactive learning environments

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