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Enhanced skeleton visualization for view invariant human action recognition.pdf

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Enhanced skeleton visualization for view invariant human action recognition.pdf

JID: PR ARTICLE IN PRESS Pattern Recognition 000 (2017) 1–17 Contents lists available at ScienceDirect Pattern Recognition journal homepage: /locate/patcog [m5G;March 11, 2017;18:41] Enhanced skeleton visualization for view invariant human action recognition Mengyuan Liu a, Hong Liu a,?, Chen Chen b a Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing 100871, China b Center for Research in Computer Vision, University of Central Florida, Orlando, FL 32816, USA article info Article history: Received 14 December 2016 Revised 18 February 2017 Accepted 25 February 2017 Available online xxx Keywords: Human action recognition View invariant Skeleton sequence abstract Human action recognition based on skeletons has wide applications in human–computer interaction and intelligent surveillance. However, view variations and noisy data bring challenges to this task. What’s more, it remains a problem to effectively represent spatio-temporal skeleton sequences. To solve these problems in one goal, this work presents an enhanced skeleton visualization method for view invariant human action recognition. Our method consists of three stages. First, a sequence-based view invariant transform is developed to eliminate the effect of view variations on spatio-temporal locations of skeleton joints. Second, the transformed skeletons are visualized as a series of color images, which implicitly encode the spatio-temporal information of skeleton joints. Furthermore, visual and motion enhancement methods are applied on color images to enhance their local patterns. Third, a convolutional neural networks-based model is adopted to extract robust and discriminative features from color images. The ?nal action class scores are generated by decision level fusion of deep features. Extensive experiments on four challenging datasets consistently demonstrate the superiority of our method. ? 2017 Elsevier Ltd. All rights reserved. 1. Introduction Human action recognit