A New Adaptive Segmental Matching Measure for Human Activity Recognition

Shahriar Shariat, Vladimir Pavlovic; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 3583-3590

Abstract


The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignmentbased method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity measures within sequence classes and hence, raises the classification performance. We also apply a bounding technique on the histogram distances to reduce the computation of the otherwise exhaustive search.

Related Material


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[bibtex]
@InProceedings{Shariat_2013_ICCV,
author = {Shariat, Shahriar and Pavlovic, Vladimir},
title = {A New Adaptive Segmental Matching Measure for Human Activity Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}