Gaussian propagation model based dense optical flow for objects tracking

Authors:
Houssam Salmane;Yassine Ruichek;Louahdi Khoudour
Affiliations:
SeT-UTBM, University of Technology Belfort-Montbeliard, Belfort Cedex, France, LEOST-IFSTTAR, French Institute of Sciences and Technology for Transport, development and networks, Villeneuve d'Ascq ...;SeT-UTBM, University of Technology Belfort-Montbeliard, Belfort Cedex, France, LEOST-IFSTTAR, French Institute of Sciences and Technology for Transport, development and networks, Villeneuve d'Ascq ...;SeT-UTBM, University of Technology Belfort-Montbeliard, Belfort Cedex, France, LEOST-IFSTTAR, French Institute of Sciences and Technology for Transport, development and networks, Villeneuve d'Ascq ...
Venue:
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Year:
2012

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Abstract

In this paper we present a method for objects tracking within a surveillance zone. The tracking process starts by detecting moving objects using Independent Component Analysis. When a set of moving objects is detected, targets are extracted basing on an energy vector comparison strategy, which consists in clustering the pixels of the detected objects. Once the targets are extracted, the tracking is performed by calculating the optical flow of the pixels of the objects. This is achieved by a Harris points based optical flow propagation, followed by a Kalman filtering based correction. Experimental results are presented to demonstrate the effectiveness of the proposed method. This work is developed within the framework of PANsafer project (Towards a safer level crossing), supported by the Frensh ANR VTT program.