A Survey of Computer Vision Based Pedestrian Detection
for Driver Assistance Systems

Jia, Hui-Xing

doi:10.1360/aas-007-0084

Cited by 16 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This monitoring technology relies on cameras installed in the vehicle to detect nearby pedestrians to assist the driving system. The process aims to assess potential risks and subsequently implement measures to ensure pedestrian safety [2].…”

Section: Pedestrian Detection In Ai Autonomous Drivingmentioning

confidence: 99%

Research on the Application of Recognition and Detection Technology in Automatic Driving

He,

Liu,

Wang

2024

HSET

View full text Add to dashboard Cite

Object detection is the cornerstone of autonomous driving systems. In autonomous driving systems, vehicle detection is critical to maintaining traffic flow and avoiding collisions. The use of deep learning technology enables the system to skillfully and accurately identify vehicles on the road. Deep learning algorithms can provide autonomous vehicles with precise environmental awareness to enable informed driving decisions. Deep learning algorithms can achieve pedestrian detection. Such algorithms can improve the safety of vehicle operations. Through deep learning models, the system is able to identify and track pedestrians, ensuring appropriate responses in different traffic scenarios. The application of detection technology enables vehicles to promptly identify and interpret road signs and traffic lights in real-time. This recognition ability helps to comprehensively understand traffic conditions and improve the ability to adapt to different road conditions. This study provides an in-depth exploration of the importance and application of deep learning technology in the field of autonomous driving. Special attention is paid to the key role played by object detection methods. and propose future development directions. Deep learning algorithms can expand the application of learning technology to handle real-time traffic conditions and achieve rapid response. Achieve the fusion of multiple sensory information by improving the local processing capabilities of edge computing. This research aims to further improve the level of intelligence inherent in autonomous driving systems.

show abstract

Section: Pedestrian Detection In Ai Autonomous Drivingmentioning

confidence: 99%

Research on the Application of Recognition and Detection Technology in Automatic Driving

He,

Liu,

Wang

2024

HSET

View full text Add to dashboard Cite

show abstract

“…Deep Neural Detection Network. Te defect detection network based on deep learning (DL) can be structurally divided into: two-stage network represented by faster RCNN [16] and one-stage network represented by singleshot multibox detector (SSD) [17] or you only look once (YOLO) [18] network [19]. Te main diference between the onestage network and the two-stage network is the former directly detects the target and predicts the class and location of the defect.…”

Section: Improved Faster Rcnn Detection Networkmentioning

confidence: 99%

Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN

Yixuan,

Dongbo,

Jiawei

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Aiming at the difficulty of automatic blade detection and the discontinuous defects on the full image, an aeroengine blade surface defect detection system based on improved faster RCNN is designed. Firstly, a dataset of blade surface defects is constructed. To solve the problem that the original faster RCNN is hard to detect tiny defects, RoI align is adopted to replace RoI pooling in the improved faster RCNN and the feature pyramid networks (FPN) combined with ResNet-50 are introduced for feature extraction. To address the issue of discontinuous defects on the full image, the nonmaximum suppression (NMS) algorithm is improved to ensure the continuity of defects. A four-degree-of-freedom (4-DOF) motion platform and an industrial camera are used to collect images of blade surfaces. The detection results generated by the improved faster RCNN are compared with the results of the unimproved method. The experimental results prove that the defect detection system based on the improved faster RCNN can realize automatic defect detection on the blade surface with high accuracy. It also solves the issues of tiny defect detection and discontinuous defects on the full result image of the blade.

show abstract

“…Because the existence of the restricting factors, such as the weaknesses of human being, false positives and false negatives, monitoring time, long response time, difficult data analysis, the development of video surveillance system is restricted in some certain [1]. But after adding the text-based information retrieval method, it gets a better retrieval result.…”

Section: Introductionmentioning

confidence: 99%

Motion Tracking and Testing Based on Improved Surendra Algorithm

Yao¹,

Singh²

2014

TOEEJ

View full text Add to dashboard Cite

Recently, due to the gradual mature of the development of computer vision, video-based monitoring and control system has become a classic practice in the field of computer vision. Traffic detection and tracking technology in intelligent video surveillance system is one of the branches of computer vision, which has gradually become a hot and new research field. Through analysis and summary of the existing detection and tracking technology, this study draws a set of target detection and tracking program at the perspective of taking photos with a single fixed camera on the road. The target in the program is the vehicle on the road. The key point of the program is to detect the target, and another is tracking. The main purpose of this study is to detect and track the moving vehicles on the road in the condition of a single fixed camera. This detection program uses the improved surendra algorithm, which is a more advanced algorithm in the algorithms of moving target detection. In all the algorithms, such as background subtraction method and the adjacent frame difference method, the improved surendra algorithm is more excellent than them. The algorithm is based on the mixed Gaussian model method and the improved adjacent frame difference. Experiment shows that the algorithm is able to track and detect the target vehicle accurately indeed. And the complexity, real-time and robustness of the algorithm are very consistent with the system design requirements of the study, so the adoption of the algorithm and the implementation of the detection system design of this study can track and detect the target vehicle well.

show abstract

A Survey of Computer Vision Based Pedestrian Detection for Driver Assistance Systems

Cited by 16 publications

References 0 publications

Research on the Application of Recognition and Detection Technology in Automatic Driving

Research on the Application of Recognition and Detection Technology in Automatic Driving

Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN

Motion Tracking and Testing Based on Improved Surendra Algorithm

Contact Info

Product

Resources

About