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Paper : Towards understanding action recognition

Author : H. Jhuang and J. Gall and S. Zuffi and C. Schmid and M. J. Black

Dataset URL

Description : Joints for the HMDB dataset (J-HMDB) is based on 928 clips from HMDB51 comprising 21 action categories. Each frame has a 2D pose annotation based on a 2D articulated human puppet model that provides scale, pose, segmentation, coarse viewpoint, and dense optical flow for the humans in action.

Number of Videos : 928

Number of Classes : 21


Evaluation: JHMDB Action classification

Description: Action classifiaction as described by authors


Result Paper Description URL Peer Reviewed Year
Result Paper Description URL Peer Reviewed Year
76.1 Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection[Mohammadreza Zolfaghari , Gabriel L. Oliveira, Nima Sedaghat, and Thomas Brox] Chained Multi-stream Network URL No 2017
71.08 Multi-region two-stream R-CNN for action detection[Xiaojiang Peng, Cordelia Schmid] MR-TS R-CNN URL Yes 2016
62.5 Finding Action Tubes[Georgia Gkioxari, Jitendra Malik] Action Tubes URL Yes 2015
72.2 P-CNN: Pose-based CNN Features for Action Recognition[Guilhem Cheron, Ivan Laptev, Cordelia Schmid] P-CNN + IDT-FV URL Yes 2015
69.03 Action Recognition with Stacked Fisher Vectors[Xiaojiang Peng, Changqing Zou, Yu Qiao, Qiang Peng] Stacked Fisher Vectors (FV+SFV) URL Yes 2014
73.3 Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition[A. Cherian, P. Koniusz, S. Gould] HOK + second-order + Trajectories URL Yes 2017
73.7 Generalized Rank Pooling for Activity Recognition[Anoop Cherian, Basura Fernando, Mehrtash Harandi, Stephen Gould] Generalized Rank Pooling + IDT-FV URL Yes 2017

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