NDLI: Human interaction recognition in the wild: Analyzing trajectory clustering from multiple-instance-learning perspective

Content Provider	IEEE Xplore Digital Library
Author	Bo Zhang Rota, P. Conci, N. De Natale, F.G.B.
Copyright Year	2015
Description	Author affiliation: DISI, Univ. of Trento, Trento, Italy (Bo Zhang; Rota, P.; Conci, N.; De Natale, F.G.B.)
Abstract	In this paper, we propose a framework to recognize complex human interactions. First, we adopt trajectories to represent human motion in a video. Then, the extracted trajectories are clustered into different groups (named as local motion patterns) using the coherent filtering algorithm. As trajectories within the same group exhibit similar motion properties (i.e., velocity, direction), we adopt the histogram of large-displacement optical flow (denoted as HO-LDOF) as the group motion feature vector. Thus, each video can be briefly represented by a collection of local motion patterns that are described by the HO-LDOF. Finally, classification is achieved using the citation-KNN, which is a typical multiple-instance-learning algorithm. Experimental results on the TV human interaction dataset and the UT human interaction dataset demonstrate the applicability of our method.
Starting Page	1
Ending Page	6
File Size	5317014
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177480
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Accuracy Tracking Standards Feature extraction Histograms Visualization multiple-instance-learning Human interaction large-displacement optical flow dense trajectory
Content Type	Text
Resource Type	Article

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