Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
A Survey of Longest Common Subsequence Algorithms
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
Informative Shape Representations for Human Action Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
Viewpoint insensitive action recognition using envelop shape
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Computer Vision and Image Understanding
Part-based motion descriptor image for human action recognition
Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We present an effective algorithm to detect essential body-joints and their corresponding atomic actions from a series of human activity data for efficient human activity recognition/classification. Our human activity data is captured by a RGB-D camera, i.e. Kinect, where human skeletons are detected and provided by the Kinect SDK. Unique in our approach is the novel encoding that can effectively convert skeleton data into a symbolic sequence representation which allows us to detect the essential atomic actions of different human activities through longest common subsequence extraction. Our experimental results show that, through atomic action detection, we can recognize human activity that consists of complicated actions. In addition, since our approach is "simple", our human activity recognition algorithm can be performed in real-time.