Abstract
In the most recent decade, researchers have lavished attention to human activity analysis, owing to its potential use in various fields. We suggest an alternative method for dealing with individual human joints (vertex positions) based on a model-based approach. The elliptic Fourier descriptors parameterize the movement formats that reflect the joint movement as inferred from walk inquiry. The heel strike data is manipulated to reduce the dimension of the parametric models. Spatial and temporal features characterize an Individual's walk, and we are capturing both. For indoor and outdoor information, the lower leg, knee, and hip joints are effectively and precisely extracted. Various experiments are done to make a marker-free inquiry, and various zones were created. The exploratory results confirmed the proposed technique’s efficacy in perceiving walking objects with a 92% accuracy in characterization rate. Our suggested methodology applies to various surveillance scenarios, including pedestrian, subway, bus stop, airport, and bank surveillance.
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Parashar, A., Parashar, A., Aski, V. (2022). Optimized Pose-Based Gait Analysis for Surveillance. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_54
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DOI: https://doi.org/10.1007/978-981-19-0475-2_54
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