[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Tian, Di; Yong, Han; Wang, Biyao; Guan, Tian; Wei, Wei

doi:10.1155/2021/5410049

Cited by 25 publications

(13 citation statements)

References 115 publications

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“…One of the CV applications needed in autonomous driving and video surveillance contexts (for example, optimizing pedestrian waiting times at signalized intersections [ 165 ]) is pedestrian detection, which is a specific application of the object recognition problem [ 166 ]. Pedestrian detection is one of the most well-established areas of CV study for ITS [ 19 , 20 , 167 , 168 ] and is used as a preliminary step to ensure traffic safety and determine pedestrian density.…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

“…In [ 166 ], the results of research into pedestrian detection using DL methods, occlusion, and multiscale problems affecting pedestrian detection were examined in detail. The authors observed that AI models developed in recent years can successfully detect pedestrians in images with high precision.…”

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

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Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

Section: Computer Vision Applications In Intelligent Transportation S...mentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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“…Pedestrian detection and tracking are the basis for pedestrian safety in ADAS and system monitoring. Various techniques have been discussed in recent years for pedestrian detection and tracking [8][9][10][11][12]. However, occlusion, low resolution, illumination, weather conditions and day timing are challenging tasks for video pedestrian detection.…”

Section: Related Workmentioning

confidence: 99%

“…Other datasets related to pedestrians like PEI, INRIA, KITTI, CityPersons, TUD, and EuroCity etc, have been discussed in the literature [9]. These datasets focus on detection and their action/activity, but no information about their direction and intention is provided.…”

Section: Datasetmentioning

confidence: 99%

Computer vision-based framework for pedestrian movement direction recognition

Monika

Singh

Chand

2023

IFS

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Pedestrians are the most critical and vulnerable moving objects on roads and public areas. Learning pedestrian movement in these areas can be helpful for their safety. To improve pedestrian safety and enable driver assistance in autonomous driver assistance systems, recognition of the pedestrian direction of motion plays an important role. Pedestrian movement direction recognition in real world monitoring and ADAS systems are challenging due to the unavailability of large annotated data. Even if labeled data is available, partial occlusion, body pose, illumination and the untrimmed nature of videos poses another problem. In this paper, we propose a framework that considers the origin and end point of the pedestrian trajectory named origin-end-point incremental clustering (OEIC). The proposed framework searches for strong spatial linkage by finding neighboring lines for every OE (origin-end) lines around the circular area of the end points. It adopts entropy and Q measure for parameter selection of radius and minimum lines for clustering. To obtain origin and end point coordinates, we perform pedestrian detection using the deep learning technique YOLOv5, followed by tracking the detected pedestrian across the frame using our proposed pedestrian tracking algorithm. We test our framework on the publicly available pedestrian movement direction recognition dataset and compare it with DBSCAN and Trajectory clustering model for its efficacy. The results show that the OEIC framework provides efficient clusters with optimal radius and minlines.

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“…The SAF RCNN [10] enhances general object detection, but general object detection improvement is constrained because object size changes are more frequent in pedestrian identification. Area proposal and classification are the two components of the two-stage pedestrian detection framework [11]. By offering innovative preselection box generation and feature extraction approaches, improving the prediction component, or both, researchers might boost the detection effect.…”

Section: Pedestrian and Group Detectionmentioning

confidence: 99%

A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd

Shaamili¹

2022

CIML

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In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considering this scenario, the smart system requires the most advanced techniques to monitor the security of the crowd. Crowd analysis is commonly divided into crowd statics and behavior analysis. This paper explores more about crowd behaviour analysis, pedestrian and group detection which describes the movements that are noticed in the crowd image. Subsequently, the issues of the current methodology of pedestrian detection, datasets, and evaluation criteria are analyzed. Keyword : Crowd Analysis, Pedestrian and group detection, deep learning, Crowd IoT analysis, Human Activity Recognition.

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[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Cited by 25 publications

References 115 publications

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Computer vision-based framework for pedestrian movement direction recognition

A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd

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