Abstract
Human recognition at distance has ever been an attractive solution for many applications. However, the challenges associated with this task, especially in the wild, are difficult to overcome. Most gait recognition methods do not take into account the shape of the body as a whole, but also a walking style, which helps to recognize a person more accurately. We offer a method for recognizing walking style at a reasonable distance of the camera so that the visual information of a walking person can be captured and analyzed. Method is based on the latest achievements in deep object detection and tracking, as well as deep networks for human action recognition, adapted for walking style recognition. We study six main categories of walking style based on head position, torso position, arm swing, stride length, and walking speed. Additionally, we analyze several clips from each video sequence for accurate recognition. Experiments show that sometimes we cannot explicitly assign a walking style to one category, but two categories such as driver and influencer describe human walking in the wild well. We achieved recognition results 82.4% Top-1 and 94.7% Top-2.
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Favorskaya, M.N., Buryachenko, V.V. (2022). Vision-Based Walking Style Recognition in the Wild. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_19
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DOI: https://doi.org/10.1007/978-981-19-3444-5_19
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