Abstract
Human Activity Recognition (HAR) from videos is an important area of computer vision research with several applications. There are a wide number of methods to classify video human activities, not without certain disadvantages such as computational cost, dataset specificity or low resistance to noise, among others. In this paper, we propose the use of the Normalized Compression Distance (NCD), as a complementary approach to identify video-based HAR. We have developed a novel ASCII video data format, as a suitable format to apply the NCD in video. For our experiments, we have used the Activities of Daily Living Dataset, to discriminate several human activities performed by different subjects. The experimental results presented in this paper show that the NCD can be used as an alternative to classical analysis of video HAR.
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Acknowledgment
This work was funded by Spanish project of MINECO/FEDER TIN2014-54580-R and TIN2017-84452-R, (http://www.mineco.gob.es/).
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Sarasa, G., Montero, A., Granados, A., Rodriguez, F.B. (2018). Compression-Based Clustering of Video Human Activity Using an ASCII Encoding. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_7
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