Recognizing Activities Via Bag of Words for Attribute Dynamics

Citation

Li, W., Yu, Q., Sawhney, H., & Vasconcelos, N. (2013, 23-28 June). Recognizing activities via bag of words for attribute dynamics. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPRW ’13), Portland, Oregon.

Abstract

In this work, we propose a novel video representation for activity recognition that models video dynamics with attributes of activities. A video sequence is decomposed into short-term segments, which are characterized by the dynamics of their attributes. These segments are modeled by a dictionary of attribute dynamics templates, which are implemented by a recently introduced generative model, the binary dynamic system (BDS). We propose methods for learning a dictionary of BDSs from a training corpus, and for quantizing attribute sequences extracted from videos into these BDS codewords. This procedure produces a representation of the video as a histogram of BDS codewords, which is denoted the bag-of-words for attribute dynamics (BoWAD). An extensive experimental evaluation reveals that this representation outperforms other state-of-the-art approaches in temporal structure modeling for complex activity recognition.


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