Publication: DA-VLAD: Discriminative action vector of locally aggregated descriptors for action recognition
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2018-09-06
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IEEE
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Abstract
In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1% and 80.1% respectively.
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This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP 2018)
Keywords
Human action recognition, VLAD, Feature encoding, Codewords, Improved Dense Trajectories (Idt)
Bibliographic citation
Murtaza, F., Yousaf, M.H. y Velastin, S.A. (2018). DA-VLAD: Discriminative Action Vector Of Locally Aggregated Descriptors for Action Recognition. In 2018 25th IEEE International Conference on Image Processing (ICIP).