DocumentCode :
2505318
Title :
MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods
Author :
Singh, Sanchit ; Velastin, Sergio A. ; Ragheb, Hossein
Author_Institution :
DIRC, Kingston Univ., Kingston upon Thames, UK
fYear :
2010
fDate :
Aug. 29 2010-Sept. 1 2010
Firstpage :
48
Lastpage :
55
Abstract :
This paper describes a body of multicamera human action video data with manually annotated silhouette data that has been generated for the purpose of evaluating silhouette-based human action recognition methods. It provides a realistic challenge to both the segmentation and human action recognition communities and can act as a benchmark to objectively compare proposed algorithms. The public multi-camera, multi-action dataset is an improvement over existing datasets (e.g. PETS, CAVIAR, soccerdataset) that have not been developed specifically for human action recognition and complements other action recognition datasets (KTH, Weizmann, IXMAS, HumanEva, CMU Motion). It consists of 17 action classes, 14 actors and 8 cameras. Each actor performs an action several times in the action zone. The paper describes the dataset and illustrates a possible approach to algorithm evaluation using a previously published action simple recognition method. In addition to showing an evaluation methodology, these results establish a baseline for other researchers to improve upon.
Keywords :
image recognition; video cameras; human action recognition method; human action video dataset; multiaction dataset; multicamera; Cameras; Classification algorithms; Feature extraction; Humans; Pixel; Training; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-8310-5
Type :
conf
DOI :
10.1109/AVSS.2010.63
Filename :
5597316
Link To Document :
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