Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
IEEE Transactions on Pattern Analysis and Machine Intelligence
A string matching approach for visual retrieval and classification
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Activities as time series of human postures
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Action recognition using context and appearance distribution features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Observing the widespread use of Kinect-like depth cameras, in this work, we investigate into the problem of using sole depth data for human action recognition and retrieval in videos. We proposed the use of simple depth descriptors without learning optimization to achieve promising performances as compatible to those of the leading methods based on color images and videos, and can be effectively applied for real-time applications. Because of the infrared nature of depth cameras, the proposed approach will be especially useful under poor lighting conditions, e.g. the surveillance environments without sufficient lighting. Meanwhile, we proposed a large Depth-included Human Action video dataset, namely DHA, which contains 357 videos of performed human actions belonging to 17 categories. To the best of our knowledge, the DHA is one of the largest depth-included video datasets of human actions.