Abstract
In this paper, we aim to detect human in video over large viewpoint changes which is very challenging due to the diversity of human appearance and motion from a wide spread of viewpoint domain compared with a common frontal viewpoint. We propose 1) a new feature called Intra-frame and Inter-frame Comparison Feature to combine both appearance and motion information, 2) an Enhanced Multiple Clusters Boost algorithm to co-cluster the samples of various viewpoints and discriminative features automatically and 3) a Multiple Video Sampling strategy to make the approach robust to human motion and frame rate changes. Due to the large amount of samples and features, we propose a two-stage tree structure detector, using only appearance in the 1st stage and both appearance and motion in the 2nd stage. Our approach is evaluated on some challenging Real-world scenes, PETS2007 dataset, ETHZ dataset and our own collected videos, which demonstrate the effectiveness and efficiency of our approach.
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Duan, G., Ai, H., Lao, S. (2011). Human Detection in Video over Large Viewpoint Changes. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_53
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DOI: https://doi.org/10.1007/978-3-642-19309-5_53
Publisher Name: Springer, Berlin, Heidelberg
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