Abstract
Coherent motions depict the collective movements of individuals in crowd, which widely exist in physical and biological systems. In recent years, similarity-based clustering algorithms especially Coherent Filtering (CF) clustering approach has gained high popularity in the field of coherent motions detection. CF finds motion clusters of different scale, density, and shape, in the presence of large amount of noise and outliers. The similarity measure function is utilized as the initial base step for determining the relationships among crowd individuals and thus detecting coherent motion from noisy time series data. In this paper, we evaluated the impact of four different similarity measure functions upon CF clustering approach, namely, Chebyshev, Euclidean, Canberra and Cosine, and the results were compared subsequently. Quantitative evaluation and comparison are conducted on synthetic data in two dimensional space. Results reveal that the Euclidean similarity function emerges as the best measure for capturing coherent motions from crowd clutters, while Cosine similarity function performs the worst.
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Acknowledgment
This work is supported by Universiti Sains Malaysia Research University Individual (RUI) Research Grant 1001/PELECT/8014056.
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Saleh, S.A.M., Suandi, S.A., Ibrahim, H. (2022). Impact of Similarity Measure Functions on the Performance of Coherent Filtering Detection. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_77
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DOI: https://doi.org/10.1007/978-981-16-8129-5_77
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