Paper Info
Title
Modeling Motion Of Body Parts For Action Recognition
Abstract
This paper presents a simple and computationally efficient framework for human action recognition based on modeling the motion of human body parts. Intuitively, a collective understanding of human part movements can lead to better understanding and representation of any human action. In this paper, we propose a generative representation of the motion of the human body parts to learn and classify human actions. The proposed representation combines the advantages of both local and global representations, encoding the relevant motion information as well as being robust to local appearance changes. Our work is motivated by the pictorial structures model and the framework of sparse representations for recognition. Human part movements are represented efficiently through quantization in the polar space. The key discrimination within each action is efficiently encoded by sparse representation to perform classification. The proposed method is evaluated on both the KTH and the UCF action datasets and the results are compared against other state-of-the-art methods.
Year
DOI
Venue
2011
10.5244/C.25.64
PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2011
Field
DocType
Citations 
Computer vision,Pattern recognition,Computer science,Action recognition,Sparse approximation,Artificial intelligence,Polar space,Generative grammar,Quantization (signal processing),Human body,Encoding (memory)
Conference
9
PageRank 
References 
Authors
0.68
29
3
Name
Order
Citations
PageRank
Khai N. Tran1392.73
Ioannis A. Kakadiaris21910203.66
Shishir K Shah350140.08