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Summary: Human Detection in Video over Large
Viewpoint Changes
Genquan Duan1
, Haizhou Ai1
, and Shihong Lao2
1
Computer Science & Technology Department, Tsinghua University, Beijing, China
ahz@mail.tsinghua.edu.cn
2
Core Technology Center, Omron Corporation, Kyoto, Japan
lao@ari.ncl.omron.co.jp
Abstract. In this paper, we aim to detect human in video over large
viewpoint changes which is very challenging due to the diversity of hu-
man appearance and motion from a wide spread of viewpoint domain
compared with a common frontal viewpoint. We propose 1) a new fea-
ture called Intra-frame and Inter-frame Comparison Feature to combine
both appearance and motion information, 2) an Enhanced Multiple Clus-
ters 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
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