Title :
Automatic people detection and counting for athletic videos classification
Author :
Panagiotakis, C. ; Ramasso, E. ; Tziritas, G. ; Rombaut, M. ; Pellerin, D.
Author_Institution :
Univ. of Crete, Heraklion
Abstract :
We propose a general framework that focuses on automatic individual/multiple people motion-shape analysis and on suitable features extraction that can be used on action/activity recognition problems under real, dynamical and unconstrained environments. We have considered various athletic videos from a single uncalibrated, possibly moving camera in order to evaluate the robustness of the proposed method. We have used an easily expanded hierarchical scheme in order to classify them to videos of individual and team sports. Robust, adaptive and independent from the camera motion, the proposed features are combined within Transferable Belief Model (TBM) framework providing a two level (frames and shot) video categorization. The experimental results of 97% individual/team sport categorization accuracy, using a dataset of more than 250 videos of athletic meetings indicate the good performance of the proposed scheme.
Keywords :
feature extraction; image classification; image motion analysis; video signal processing; athletic videos classification; automatic people detection; features extraction; motion-shape analysis; sport categorization accuracy; transferable belief model framework; Cameras; Computer science; Data mining; Humans; Motion analysis; Object detection; Robustness; Shape; Surveillance; Videos;
Conference_Titel :
Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on
Conference_Location :
London
Print_ISBN :
978-1-4244-1696-7
Electronic_ISBN :
978-1-4244-1696-7
DOI :
10.1109/AVSS.2007.4425349