This article presents a two-staged approach to recognize pedestrians in video sequences on board of a moving vehicle. The system combines the advantages of two feature families by splitting the recognition process into two stages: In the first stage, a fast search mechanism based on simple features is applied to detect interesting regions. The second stage uses a computationally more expensive, but also more accurate set of features on these regions to classify them into pedestrian and non-pedestrian. We compared various feature extraction configurations of different complexities regarding classification performance and speed. The complete system was evaluated on a number of labeled test videos taken from real-world drives and also compared against a publicly available pedestrian detector. This first system version analyzes only single image frames without using any temporal information like tracking. Still, it achieves good recognition performance at reasonable run time.
A two-staged approach to vision-based pedestrian recognition using Haar and HOG features
2008 IEEE Intelligent Vehicles Symposium ; 554-559
2008-06-01
376486 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A Two-Staged Approach to Vision-Based Pedestrian Recognition Using Haar and HOG Features
British Library Conference Proceedings | 2008
|Taylor & Francis Verlag | 2016
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