Part-based Pedestrian Detection and Feature-based Tracking for Driver Assistance: Real-Time, Robust Algorithms and Evaluation

Antonio Prioletti, Andreas Møgelmose, Paolo Grislieri, Mohan Trivedi, Alberto Broggi, Thomas B. Moeslund

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

97 Citationer (Scopus)
1110 Downloads (Pure)

Abstract

Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in the last ten years and detectors based on classifiers have gained a special place among the different approaches presented. This paper presents a state-of-the-art pedestrian detection system based on a two-stage classifier. Candidates are extracted with a Haar cascade classifier trained with the Daimler Detection Benchmark data set and then validated through a part-based histogram-of-oriented-gradient (HOG) classifier with the aim of lowering the number of false positives. The surviving candidates are then filtered with feature-based tracking to enhance the recognition robustness and improve the results' stability. The system has been implemented on a prototype vehicle and offers high performance in terms of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Intelligent Transportation Systems
Vol/bind14
Udgave nummer3
Sider (fra-til)1346-1359
Antal sider14
ISSN1524-9050
DOI
StatusUdgivet - 2013

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