Pedestrian Detection Using Global-Local Motion Patterns View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Dhiraj Goel , Tsuhan Chen

ABSTRACT

We propose a novel learning strategy called Global-Local Motion Pattern Classification (GLMPC) to localize pedestrian-like motion patterns in videos. Instead of modeling such patterns as a single class that alone can lead to high intra-class variability, three meaningful partitions are considered - left, right and frontal motion. An AdaBoost classifier based on the most discriminative eigenflow weak classifiers is learnt for each of these subsets separately. Furthermore, a linear three-class SVM classifier is trained to estimate the global motion direction. To detect pedestrians in a given image sequence, the candidate optical flow sub-windows are tested by estimating the global motion direction followed by feeding to the matched AdaBoost classifier. The comparison with two baseline algorithms including the degenerate case of a single motion class shows an improvement of 37% in false positive rate. More... »

PAGES

220-229

References to SciGraph publications

Book

TITLE

Computer Vision – ACCV 2007

ISBN

978-3-540-76385-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-76386-4_20

DOI

http://dx.doi.org/10.1007/978-3-540-76386-4_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048328519


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