Efficient Traffic Accident Warning Based on Unsupervised Prediction Framework

Zhou, Yunfeng; Xie, Kai; Zhang, Xinyu; Wen, Chang; He, Jing

doi:10.1109/access.2021.3077120

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…The CNN/GAN image estimate literature almost exclusively uses MRI data, with PET or CT being the two main modalities. Literature [8] created the first generative adversarial network (GAN) in 2014, which included a generator G and a discriminator D. The generator takes noise z from distribution as input, maps it to the data space, records the data distribution of the actual sample x, and creates a sample GðzÞ that looks like the original data. The produced samples and the real samples are sent to the discriminator, and the purpose is to categorize the generated samples GðzÞ as false and the actual samples as true.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] CNN‐Based Cross‐Modal Residual Network for Image Synthesis

Kumar

Bhatnagar

Jain

et al. 2022

BioMed Research International

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This study attempts to address the issue that present cross-modal image synthesis algorithms do not capture the spatial and structural information of human tissues effectively. As a consequence, the resulting photos include flaws including fuzzy edges and a poor signal-to-noise ratio. The authors offer a cross-sectional technique that combines residual modules with generative adversarial networks. The approach incorporates an enhanced residual initial module and attention mechanism into the generator network, reducing the number of parameters and improving the generator’s feature learning capabilities. To boost discriminant performance, the discriminator employs a multiscale discriminator. A multilevel structural similarity loss is included in the loss function to improve picture contrast preservation. On the ADNI data set, the algorithm is compared to the mainstream algorithms. The experimental findings reveal that the synthetic PET image’s MAE index has dropped while the SSIM and PSNR indexes have improved. The experimental findings suggest that the proposed model may maintain picture structural information while improving image quality in both visual and objective measures. The residue initial module and attention mechanism are employed to increase the generator’s capacity for learning, while the multiscale discriminator is utilized to improve the model’s discriminative performance. The enhanced method in this study can maintain the structure and contrast information of the picture, according to comparative experimental findings using the ADNI dataset. The produced picture is hence more aesthetically similar to the genuine print.

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Section: Related Workmentioning

confidence: 99%

[Retracted] CNN‐Based Cross‐Modal Residual Network for Image Synthesis

Kumar

Bhatnagar

Jain

et al. 2022

BioMed Research International

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“…M. Ichiki et al [12] used features such as dynamic obstacle presence and static road information by combining semantic segmentation and object detection. Object detection is also used in FRPN [15] and Z. Zhou et al [14], which use changes in the size of the detected bounding box and shifts in the center of gravity. SSC [13] proposed an unsupervised accident prediction method using the predictions of object movement and whole frames.…”

Section: ) Models Based On Motion and Object Featuresmentioning

confidence: 99%

Accident Prediction Model Using Divergence Between Visual Attention and Focus of Expansion in Vehicle-Mounted Camera Images

Maruyama,

Ohashi

2023

IEEE Access

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Recently, accident prediction models, which predict the occurrence of traffic accidents through deep learning algorithms have been proposed. The application of these models demands both high precision and visualization of the decision basis applied. Current models use the motion features of objects in the surrounding environment, but they do not predict well when the motion feature of the risk factor is small. Meanwhile, drivers can avoid accidents because they utilize visual attention functions. This study focuses on the divergence between visual attention and focus of expansion (FOE), which are highly correlated in normal driving situations, as the basis for an accident prediction method. The proposed model can visualize decision basis with high accuracy, even when the motion feature of the risk factors is small, by combining it with Dynamic-Spatial-Attention, a deep-learning-based accident prediction method. In this experiment, we classified data from the Dashcam Accident Dataset, a widely used accident dataset, into categories of accidents. Using the Dashcam Accident Dataset, the proposed method achieves higher accident prediction performance in categories for which the motion feature of risk factors tends to be small while maintaining the same accident prediction performance as achieved by the baseline Dynamic-Spatial-Attention method in categories for which the motion feature of risk factors tends to be large. In addition, the proposed method visualizes the risk factors using visual attention and FOE to provide a visual explanation of the decision basis.INDEX TERMS Deep learning, driver visual attention, focus of expansion, traffic accident prediction I. INTRODUCTION

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“…Two input images are put into each sub-network to create two outputs and calculate distance through two outputs. Zhou et al conducted research on defensive and unmanned driving [94] and proposed a Fusion-Residual Predictive Network (FRPN) framework to measure the degree of risk on the road.…”

Section: Appearance Learningmentioning

confidence: 99%

Multiple Object Tracking in Deep Learning Approaches: A Survey

et al. 2021

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Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

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Efficient Traffic Accident Warning Based on Unsupervised Prediction Framework

Cited by 13 publications

References 26 publications

[Retracted] CNN‐Based Cross‐Modal Residual Network for Image Synthesis

[Retracted] CNN‐Based Cross‐Modal Residual Network for Image Synthesis

Accident Prediction Model Using Divergence Between Visual Attention and Focus of Expansion in Vehicle-Mounted Camera Images

Multiple Object Tracking in Deep Learning Approaches: A Survey

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