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Design of a digital library for human movement
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Source International Conference on Digital Libraries archive
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries table of contents
Roanoke, Virginia, United States
Pages: 300 - 309  
Year of Publication: 2001
ISBN:1-58113-345-6
Authors
Jezekiel Ben-Arie  EECS Department M/C 154m, University of Illinois at Chicago, 851 S. Morgan St., SEO 1120, Chicago, IL
Purvin Pandit  EECS Department, University of Illinois at Chicago
ShyamSundar Rajaram  EECS Department, University of Illinois at Chicago
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper is focused on a central aspect in the design of our planned digital library for human movement, i.e. on the aspect of representation and recognition of human activity from video data. The method of representation is important since it has a major impact on the design of all the other building blocks of our system such as the user interface/query block or the activity recognition/storage block. In this paper we evaluate a representation method for human movement that is based on sequences of angular poses and angular velocities of the human skeletal joints, for storage and retrieval of human actions in video databases. The choice of a representation method plays an important role in the database structure, search methods, storage efficiency etc.. For this representation, we develop a novel approach for complex human activity recognition by employing multidimensional indexing combined with temporal or sequential correlation. This scheme is then evaluated with respect to its efficiency in storage and retrieval.For the indexing we use postures of humans in videos that are decomposed into a set of multidimensional tuples which represent the poses/velocities of human body parts such as arms, legs and torso. Three novel methods for human activity recognition are theoretically and experimentally compared. The methods require only a few sparsely sampled human postures. We also achieve speed invariant recognition of activities by eliminating the time factor and replacing it with sequence information. The indexing approach also provides robust recognition and an efficient storage/retrieval of all the activities in a small set of hash tables.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jezekiel Ben-Arie: colleagues
Purvin Pandit: colleagues
ShyamSundar Rajaram: colleagues