A review on vision-based pedestrian detection

Zheng, Gang; Chen, Youbin

doi:10.1109/ghtce.2012.6490122

Cited by 15 publications

(5 citation statements)

References 53 publications

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“…Pedestrian-vehicle interaction is a topic that has been under study for the past years in the context of intelligent vehicle environments. As a result, many surveys have been published on pedestrian detection within vehicle environments, using different sensors, cameras, and artificial intelligence algorithms [12][13][14][15][16].…”

Section: State Of the Artmentioning

confidence: 99%

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Guerrero-Ibañez,

Contreras-Castillo,

Amezcua-Valdovinos

et al. 2023

Machines

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Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.

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Section: State Of the Artmentioning

confidence: 99%

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Guerrero-Ibañez,

Contreras-Castillo,

Amezcua-Valdovinos

et al. 2023

Machines

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“…Work in [ 169 ] has succinctly summarized the challenges in pedestrian detection considering the resolution, range, and field-of-view of various on-borad sensors like radar, lidar, or omni-directional cameras with various types of hardwares. In addition, work in [ 170 ] has focused the survey of challenges of pedestrian detection with vision based sensors which are very prominent recently. In adverse conditions like low illumination, night, snowfall, and rain, it is further difficult to detect the dynamic entities while work has been done in this regards by using thermal images [ 171 ].…”

Section: Challengesmentioning

confidence: 99%

Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

Ravankar

Kobayashi

et al. 2018

Sensors

235

110

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Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.

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“…The fast development of wireless communications and internet of things illuminates many intelligent applications for secure and convenient daily life [1][2][3][4][5]. There is a lot of space for pedestrian detection in all fields [6][7][8][9]. For example, in the autonomous areas, accurate and timely target detection can make autonomous driving technology a reality [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Pedestrian Intrusion Detection and Warning Method for Intelligent Traffic Security

2022

KSII TIIS

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As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

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A review on vision-based pedestrian detection

Cited by 15 publications

References 53 publications

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

A Lightweight Pedestrian Intrusion Detection and Warning Method for Intelligent Traffic Security

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