NDLI: MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods

Content Provider	IEEE Xplore Digital Library
Author	Singh, S. Velastin, S.A. Ragheb, H.
Copyright Year	2010
Abstract	This paper describes a body of multicamera humanaction video data with manually annotated silhouette datathat has been generated for the purpose of evaluatingsilhouette-based human action recognition methods. Itprovides a realistic challenge to both the segmentationand human action recognition communities and can act asa benchmark to objectively compare proposed algorithms.The public multi-camera, multi-action dataset is animprovement over existing datasets (e.g. PETS, CAVIAR,soccerdataset) that have not been developed specificallyfor human action recognition and complements otheraction recognition datasets (KTH, Weizmann, IXMAS,HumanEva, CMU Motion). It consists of 17 action classes,14 actors and 8 cameras. Each actor performs an actionseveral times in the action zone. The paper describes thedataset and illustrates a possible approach to algorithmevaluation using a previously published action simplerecognition method. In addition to showing an evaluationmethodology, these results establish a baseline for otherresearchers to improve upon.
Starting Page	48
Ending Page	55
File Size	636538
Page Count	8
File Format	PDF
ISBN	9781424483105
e-ISBN	9781424483112
DOI	10.1109/AVSS.2010.63
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Humans Training data Cameras Feature extraction Classification algorithms Pixel
Content Type	Text
Resource Type	Article

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