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 threeclass 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Goel, D., & Chen, T. (2007). Pedestrian detection using global-local motion patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4843 LNCS, pp. 220–229). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_20
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