Abstract
Recognizing human activities from video sequences or sensor data is a challenging task in computer vision. Background clutter, partial occlusion, changes in viewpoint, lighting, and appearance are creating bottlenecks in the recognition of activity. In this paper, we provide a comprehensive review by categorizing the activity recognition approaches that have been applied on multivariate time series data. The review provides insights of each method, research issues and performance issue.
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Anitha Rani, I., Vadivel, A. (2020). Human Activity Recognition on Multivariate Time Series Data: A Technical Review. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_37
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DOI: https://doi.org/10.1007/978-981-15-1420-3_37
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