Pedestrian detection system based on stereo vision for mobile robot

Stereo Vision by Combination of Machine-Learning Techniques for Pedestrian Detection at Intersections Utilizing Surround-View Cameras

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2020.p0494 ◽

2020 ◽

Vol 32 (3) ◽

pp. 494-502 ◽

Cited By ~ 1

Author(s):

Tokihiko Akita ◽

Yuji Yamauchi ◽

Hironobu Fujiyoshi ◽

◽

Keyword(s):

Machine Learning ◽

Stereo Vision ◽

Traffic Accidents ◽

Detection System ◽

Pedestrian Detection ◽

Field Of View ◽

Machine Learning Techniques ◽

Support Vector ◽

Wide Field ◽

Learning Techniques

The frequency of pedestrian traffic accidents continues to increase in Japan. Thus, a driver assistance system is expected to reduce the number of accidents. However, it is difficult for the current environmental recognition sensors to detect crossing pedestrians when turning at intersections, owing to the field of view and the cost. We propose a pedestrian detection system that utilizes surround-view fisheye cameras with a wide field of view. The system can be realized at low cost if the fisheye cameras are already equipped. It is necessary to detect the positioning of pedestrians accurately because more precise prediction of future collision points is required at intersections. Stereo vision is suitable for this purpose. However, there are some concerns regarding realizing stereo vision using fisheye cameras due to the distortion of the lens, asynchronous capturing, and fluctuating camera postures. As a countermeasure, we propose a novel method combining various machine-learning techniques. The D-Brief with histogram of oriented gradients and normalized cross-correlation are combined by a support-vector machine for stereo matching. A random forest was adopted to discriminate the pedestrians from noise in the 3D reconstructed point cloud. We evaluated this for images of crossing pedestrians at actual intersections. A tracking rate of 96.0% was achieved as the evaluation result. It was verified that this algorithm can accurately detect a pedestrian with an average position error of 0.17 m.

Download Full-text

Stereo Vision Based Worker Detection System for Excavator

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building the Future in Automation and Robotics ◽

10.22260/isarc2013/0110 ◽

2013 ◽

Cited By ~ 3

Author(s):

H. Ishimoto ◽

T. Tsubouchi

Keyword(s):

Stereo Vision ◽

Detection System

Download Full-text

Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision

IET Intelligent Transport Systems ◽

10.1049/iet-its.2019.0367 ◽

2020 ◽

Vol 14 (10) ◽

pp. 1319-1327 ◽

Cited By ~ 1

Author(s):

Pedro Augusto Pinho Ferraz ◽

Bernardo Augusto Godinho de Oliveira ◽

Flávia Magalhães Freitas Ferreira ◽

Carlos Augusto Paiva da Silva Martins

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Stereo Vision ◽

Pedestrian Detection

Download Full-text

Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽

10.3390/s21051820 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1820

Author(s):

Xiaotao Shao ◽

Qing Wang ◽

Wei Yang ◽

Yun Chen ◽

Yi Xie ◽

...

Keyword(s):

Semantic Information ◽

Detection System ◽

Pedestrian Detection ◽

Detection Accuracy ◽

The Public ◽

Scale Feature ◽

Detection Algorithms ◽

Multi Scale ◽

Art Works ◽

Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

Download Full-text