基于无线传感器网络的智能交通系统行人实时检测与分类

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
Saureng Kumar, S. Sharma, Ramendra Kumar
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引用次数: 1

摘要

行人安全已经成为发展社会特别是道路交通的一个重要考虑因素,一个小时的智能交通需求是解决方案。印度以11%的全球道路事故位居世界首位。有了这些数据,我们已经朝着计算机视觉应用的方向发展,为智能交通系统(ITS)提供高效准确的行人检测。该研究的重要应用是机器人开发、交通管理和控制、无人驾驶(UVD)、智能监控系统和行人自动检测系统。许多研究都集中在行人检测上,但仍需要可持续的解决方案驱动的研究来克服道路事故。我们提出了一种基于无线传感器的行人检测系统,该系统对行人活动的实时集合进行分类,并对来自传感器节点的相互接收信号强度(RSS)进行采样。我们应用了面向梯度直方图(HOG)描述符算法K近邻、决策树和线性支持向量机来测量目标的性能和预测。此外,这些算法在不同方面进行了比较分析。使用481个样本对线性支持向量机算法进行了训练。该性能实现了98.90%的准确率,并取得了优异的结果,最大精度为0.99,召回率为0.98,F分数为0.95,错误率为2%。该模型的预测结果表明,该模型可用于智能交通系统。最后,讨论了行人检测的局限性和挑战,为未来进行有效行人检测的研究方向提供了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System
Pedestrian safety has become a critical consideration in developing society especially road traffic, an intelligent transportation need of the hour is the solution left. India tops the world with 11% of global road accidents. With this data, we have moved in the direction of computer vision applications for efficient and accurate pedestrian detection for intelligent transportation systems (ITS). The important application of this research is robot development, traffic management and control, unmanned vehicle driving (UVD), intelligent monitoring and surveillance system, and automatic pedestrian detection system. Much research has focused on pedestrian detection, but sustainable solution-driven research must still be required to overcome road accidents. We have proposed a wireless sensor network-based pedestrian detection system that classifies the real-time set of pedestrian activity and samples the reciprocally received signal strength (RSS) from the sensor node. We applied a histogram of oriented gradient (HOG) descriptor algorithm K-nearest neighbor, decision tree and linear support vector machine to measure the performance and prediction of the target. Also, these algorithms have performed a comparative analysis under different aspects. The linear support vector machine algorithm was trained with 481 samples. The performance achieves the accuracy of 98.90%and has accomplished superior results with a maximum precision of 0.99, recall of 0.98, and F-score of 0.95 with 2% error rate. The model’s prediction indicates that it can be used in the intelligent transportation system. Finally, the limitation and the challenges discussed to provide an outlook for future research direction to perform effective pedestrian detection.
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来源期刊
CiteScore
3.80
自引率
6.20%
发文量
57
审稿时长
20 weeks
期刊介绍: IJMEMS is a peer reviewed international journal aiming on both the theoretical and practical aspects of mathematical, engineering and management sciences. The original, not-previously published, research manuscripts on topics such as the following (but not limited to) will be considered for publication: *Mathematical Sciences- applied mathematics and allied fields, operations research, mathematical statistics. *Engineering Sciences- computer science engineering, mechanical engineering, information technology engineering, civil engineering, aeronautical engineering, industrial engineering, systems engineering, reliability engineering, production engineering. *Management Sciences- engineering management, risk management, business models, supply chain management.
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