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Human activity prediction using temporally-weighted generalized time warping
Feb 17, 2017Author:
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Title: Human activity prediction using temporally-weighted generalized time warping
Authors: Wang, HR; Yang, WK; Yuan, CF; Ling, HB; Hu, WM
Author Full Names: Wang, Haoran; Yang, Wankou; Yuan, Chunfeng; Ling, Haibin; Hu, Weiming
Source: NEUROCOMPUTING, 225 139-147; 10.1016/j.neucom.2016.11.004 FEB 15 2017
Language: English
Abstract: Different from traditional human activity recognition, human activity prediction aims to recognize an unfinished activity, typically in absence of explicit temporal progress status. In this paper, we propose a new human activity prediction approach by extending the recently proposed generalized time warping (GTW) [20], which allows an efficient and flexible alignment of two or more multi-dimensional time series. More specifically, for each activity video, either complete or incomplete, we first decompose it into a sequence of short video segments. Then, we represent each segment by the local spatial-temporal statistics using the classical bag-of visual -words model. In this way, the comparison between a query sequence (i.e., containing an incomplete activity) and a reference sequence (i.e., containing a full activity) boils down to the problem of aligning their corresponding segment sequences. While GTW treats different portions of a sequence as equally important, our task is in favor of early portions since an incomplete activity video always aligns from the beginning of a complete one. Thus motivated, we develop a temporally-weighted GTW (TGTW) algorithm for the activity prediction problem by encouraging alignment in the early portion of an activity sequence. Finally, the similarity derived from TGTW is combined with the k-nearest neighbors algorithm for predicting the activity class of an input sequence. The proposed approach is evaluated on several publicly available datasets in comparison with state-of-the-art approaches. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.
ISSN: 0925-2312
eISSN: 1872-8286
IDS Number: EI0KW
Unique ID: WOS:000392164400014