Integrating 3D structure into traffic scene understanding with RGB-D data

Xia, Yingjie; Xu, Weiwei; Zhang, Luming; Shi, Xiaobo; Mao, Kuang

doi:10.1016/j.neucom.2014.05.091

Cited by 26 publications

(5 citation statements)

References 47 publications

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“…RGB data provides textural and appearance information of the object being detected, while the depth data (e.g., thermal data) can provide additional information of the shape of the object [106]. This approach was implemented in [106] by fusing an RGB camera with a depth camera, which detects heat signatures using a thermal sensor [116,117].…”

Section: Thermal-based Sensorsmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

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Section: Thermal-based Sensorsmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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“…Due to 2D features are ineffective in some scenarios, 3D features are adopted for dealing with the occlusion cases [12]. By learning RGB-D data of traffic scene, Xia et al [13] combine the depth-based segmentation method and the tracking method based on 2D features and detect and classify traffic-related objects, and the overlapping issues are solved effectively.…”

Section: Occlusion Detection and Segmentationmentioning

confidence: 99%

APPOS: An adaptive partial occlusion segmentation method for multiple vehicles tracking

Zhao

Xia

et al. 2016

Journal of Visual Communication and Image Representation

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“…Some studies have shown that distracted driving and fatigue driving are prone to traffic accidents. Based on the studies, the number of the dead in road traffic accidents has been increasing every year [1,2]. Therefore, it is urgently necessary to study Advanced Driving Assistance System (ADAS), Intelligent Transportation System (ITS) and Traffic sign Recognition (TSR).…”

Section: Introductionmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

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Object detection has made great progress with the rise of convolutional neural networks in recent years. Traffic sign detection is a research hotspot for object detection tasks. The existing detection models have the problems of inaccurate positioning and low classification accuracy when detecting small traffic signs. To address these issues, in this paper, we propose a small traffic sign detection method based on YOLOv4. Specifically, we design an attention-based feature fusion module including attention spatial pyramid pooling (ASPP) and attention path aggregation networks (APAN). ASPP highlights useful small object information and suppresses invalid interference information in the background. APAN reduces information loss during feature fusion. A large number of experimental results on public datasets show that the method in this paper improves the detection performance of the model. In terms of small traffic sign detection, the method improves YOLOv4 by 12 mAP, and meets the real-time requirements of automatic driving detection (more than 50 FPS).

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Integrating 3D structure into traffic scene understanding with RGB-D data

Cited by 26 publications

References 47 publications

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

APPOS: An adaptive partial occlusion segmentation method for multiple vehicles tracking

Attention feature fusion network for small traffic sign detection

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