Detection of pedestrian crossing road: A study on pedestrian pose recognition

Hariyono, Joko; Jo, Kang-Hyun

doi:10.1016/j.neucom.2016.12.050

Cited by 41 publications

(14 citation statements)

References 22 publications

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“…However, published studies of pedestrian walking behaviour under different lighting applications are rare. Existing studies are largely limited to either assessing pedestrian flow under different artificial lighting applications (e.g., [5,34,35]) or analysing pedestrians' crossing behaviour and modelling it from a traffic safety perspective (e.g., [36]). Hence, more fine-tuned observational methods of evaluating pedestrian movement behaviour are required.…”

Section: Previous Researchmentioning

confidence: 99%

Video Analysis of Pedestrian Movement (VAPM) under Different Lighting Conditions—Method Exploration

2020

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When daylight hours are limited, pedestrians are dependent on appropriate outdoor lighting. Although new city lighting applications must consider both energy usage and pedestrian responses, current methods used to capture pedestrian walking behaviour during dark conditions in real settings are limited. This study reports on the development and evaluation of a video-based method that analyses pedestrians’ microscopic movements (VAPM—video analysis of pedestrian movements), including placement and speed, in an artificially lit outdoor environment. In a field study utilising between-subjects design, 62 pedestrians walked along the same path under two different lighting applications. VAPM accurately discriminated pedestrians’ microscopic movements in the two lighting applications. By incorporating methodological triangulation, VAPM successfully complemented observer-based assessments of pedestrians’ perceptions and evaluations of the two lighting applications. It is suggested that in evaluations of pedestrian responses to city lighting applications, observer-based assessments could be successfully combined with an analysis of actual pedestrian movement while walking in the lit environment. However, prior to employing a large-scale application of VAPM, the methodology needs to be further adapted for use with drones and integration into smart city lighting systems.

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Section: Previous Researchmentioning

confidence: 99%

Video Analysis of Pedestrian Movement (VAPM) under Different Lighting Conditions—Method Exploration

2020

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“…In the other solution of pedestrian action prediction proposed [7,8,13], our most recent article [16] addresses the interaction between cars and pedestrians. However, in this proposal of paper, there are only 3 cases in which pedestrian action features are extracted, classified and predicted, including pedestrian crossing, pedestrian waiting and pedestrian walking.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Activity Prediction Based on Semantic Segmentation and Hybrid of Machines

Tran

Hoang

Pham

et al. 2018

JCC

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The article presents an advanced driver assistance system (ADAS) based on a situational recognition solution and provides alert levels in the context of actual traffic. The solution is a process in which a single image is segmented to detect pedestrians’ position as well as extract features of pedestrian posture to predict the action. The main purpose of this process is to improve accuracy and provide warning levels, which supports autonomous vehicle navigation to avoid collisions. The process of the situation prediction and issuing of warning levels consists of two phases: (1) Segmenting in order to definite the located pedestrians and other objects in traffic environment, (2) Judging the situation according to the position and posture of pedestrians in traffic. The accuracy rate of the action prediction is 99.59% and the speed is 5 frames per second.

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“…Over last few years, various works has been reported in pedestrian detection for driver assistance system. In this regard, J. Hariyono and K. Hyun Jo suggested [7] a model for detection of pedestrian crossing the road. In which pedestrian pose and lateral speed are recognized using spatial body mass ratio.…”

Section: Literature Reviewmentioning

confidence: 99%

Pedestrian Detection Approach for Driver Assisted System using Haar based Cascade Classifiers

Chhajro¹,

Kumar²,

Rind³

et al. 2018

ijacsa

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Object detection and tracking with the aid of computer vision is a most challenging task in the context of Driver Assistant System (DAS) for vehicles. This paper presents pedestrians detection techique using Haar-Like Features. The main aim of this research is to develop a detection system for vehicle drivers that will intimate them in advance for pedestrian's movement when they are crossing the zebra region or passing nearby to it along the road. For this purpose, dataset of 1000 images have been taken via CCTV camera which was mounted for road monitoring. A Haar based cascade classifiers have been implemented over images. And system is trained for positive (with people) and negative (without people) image samples, respectively. After testing, the obtained results show that it attained 90% accuracy while pedestrian detection. The proposed work provides significant contribution in order to reduce the road accidents as well as ensure the safety measurement for road management.

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Detection of pedestrian crossing road: A study on pedestrian pose recognition

Cited by 41 publications

References 22 publications

Video Analysis of Pedestrian Movement (VAPM) under Different Lighting Conditions—Method Exploration

Video Analysis of Pedestrian Movement (VAPM) under Different Lighting Conditions—Method Exploration

Pedestrian Activity Prediction Based on Semantic Segmentation and Hybrid of Machines

Pedestrian Detection Approach for Driver Assisted System using Haar based Cascade Classifiers

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