Human Action Recognition Based on Tracking Features

Visual recognition of human actions in image sequences is an active field of research. However, most recent published methods use complex models and heuristics of the human body as well as to classify their actions. Our approach follows a different strategy. It is based on simple feature extraction from descriptors obtained from a visual tracking system. The tracking system is able to bring some useful information like position and size of the subject at every time step of a sequence, and in this paper we show that, the evolution of some of these features is enough to classify an action in most of the cases.

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[2]  William E. Hart,et al.  Recent Advances in Memetic Algorithms , 2008 .

[3]  Martial Hebert,et al.  Efficient visual event detection using volumetric features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[4]  Luc Van Gool,et al.  Action snippets: How many frames does human action recognition require? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  P. Fearnhead,et al.  Building Robust Simulation-based Filters for Evolving Data Sets , 2007 .

[6]  Rama Chellappa,et al.  View invariants for human action recognition , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[7]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[8]  Liang Wang,et al.  Human action recognition by feature-reduced Gaussian process classification , 2009, Pattern Recognit. Lett..

[9]  Liang Wang,et al.  Visual learning and recognition of sequential data manifolds with applications to human movement analysis , 2008, Comput. Vis. Image Underst..

[10]  Mohiuddin Ahmad,et al.  Human action recognition using shape and CLG-motion flow from multi-view image sequences , 2008, Pattern Recognit..

[11]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, ICPR 2004.

[12]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Mubarak Shah,et al.  Chaotic Invariants for Human Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Pablo Moscato,et al.  A Gentle Introduction to Memetic Algorithms , 2003, Handbook of Metaheuristics.

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Ramakant Nevatia,et al.  Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[18]  Pablo Moscato,et al.  Memetic algorithms: a short introduction , 1999 .

[19]  Juan José Pantrigo,et al.  Multiple and variable target visual tracking for video-surveillance applications , 2010, Pattern Recognit. Lett..