Abstract
Complex action recognition is a hot topic in computer vision. When training a robust model, a large amount of labeled data is required. However, labeling complex actions is often time-consuming and expensive. Considering that each complex action is composed of a sequence of simple actions, we propose a new perspective to provide more information during training in order to solve the problem of insufficient labeled data. The probability matrix is then designed by manual annotation, which encodes a probability distribution of simple actions in complex actions. So the probability matrix is only available during training but unavailable during testing. Finally, a probability matrix is regared as privileged information in a SVM+ framework, and we regard this setting as probability matrix SVM+(pmSVM+). To validate the proposed model, extensive experiments are carried out on complex action datasets. Experiment results show the effectiveness of pmSVM+ for complex action recognition.
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Acknowledgment
This work is supported in part by the National Natural Science Founding of China (61171142, 61401163, U1636218), Science and Technology Planning Project of Guangdong Province of China (2014B010111003, 2014B010111006), the Fundamental Research Funds for the Central Universities (2017MS045), and Guangzhou Key Lab of Body Data Science (201605030011).
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Liu, F., Xu, X., Qing, C., Jin, J. (2018). Probability Matrix SVM+ Learning for Complex Action Recognition. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_39
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DOI: https://doi.org/10.1007/978-981-10-8530-7_39
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