Tracking and data association
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A survey on vision-based human action recognition
Image and Vision Computing
Human computing and machine understanding of human behavior: a survey
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we address the problem of activity detection in unsegmented image sequences. Our main contribution is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic ensembles of feature descriptors. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. We use boosting in order to select characteristic ensembles per class. This leads to a set of class specific codebooks where each codeword is an ensemble of features. During training, we store the spatial positions of the codeword ensembles with respect to a set of reference points, and their temporal positions with respect to the start and end of the action instance. During testing, each activated codeword casts votes concerning the spatiotemporal position and extend of the action, using the information stored during training. Mean Shift mode estimation in the voting space provides the most probable hypotheses concerning the localization of the subjects at each frame, as well as the extend of the activities depicted in the image sequences. We present experimental results for a number of publicly available datasets, that demonstrate the efficiency of the proposed method in localizing and classifying human activities.