Abstract
This paper reports the Cooking Activity Recognition Challenge by team Rit’s cooking held at International Conference on Activity and Behavior Computing (ABC 2020). Our approach leverages the convolutional layer and LSTM to recognize macro activities (recipe), and micro activities (body motion). For micro activities consisting of multiple labels in a segment, loss is calculated using BCEWithLogistsLoss function in PyTorch for each body part, and then the final decision is made by majority vote of classification results for each body part.
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Fujii, A., Kajiwara, D., Murao, K. (2021). Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote. In: Ahad, M.A.R., Lago, P., Inoue, S. (eds) Human Activity Recognition Challenge. Smart Innovation, Systems and Technologies, vol 199. Springer, Singapore. https://doi.org/10.1007/978-981-15-8269-1_8
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DOI: https://doi.org/10.1007/978-981-15-8269-1_8
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