Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier

This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in what can be detected, a combination of visual cues is used in conjunction with edge-based features and colour information as a basis for tr...

Full description

Saved in:
Bibliographic Details
Published in:Pattern analysis and applications : PAA 2020-05, Vol.23 (2), p.751-769
Main Authors: Braik, Malik, Al-Zoubi, Hussein, Al-Hiary, Heba
Format: Article
Language:English
Subjects:
Classifiers
Color
Computer Science
Computer vision
Contours
Cues
Detectors
Histograms
Image detection
Machine learning
Microprocessors
Pattern Recognition
Pedestrians
Pixels
Real time
Reliability
Sensors
Shape
Street furniture
Systems analysis
Theoretical Advances
Vision systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in what can be detected, a combination of visual cues is used in conjunction with edge-based features and colour information as a basis for training a random forest (RF) classifier to detect the local contour cues for pedestrian images. To achieve a real-time detection rate, the contour cues, edge-based features and colour information are incorporated and then trained using a cascade RF classifier with a census transform histogram visual descriptor that implicitly captures the global contours of the pedestrians. The contour detector favourably exceeded previous leading contour detectors and achieved a 95% detection rate. The reliability and specificity of the pedestrian detector are demonstrated on more than 5000 positive images containing street furniture, lamp posts and trees, structures that are frequently confused with persons by computer vision systems. Evaluation with over 220 video sequences with 640 × 480 pixel resolution presented a true positive rate of 96%. The proposed pedestrian detector outperforms previous competitive pedestrian detectors on many varied person data sets. The speed of execution in a robot is about 62 ms per frame for images of 640 × 480 pixels on an Intel Core i3-2310M™ processor running at 2.10 GHz with a RAM of 4 GB.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-019-00835-x