This papers tests the relevance of interest points to predict eye movements of subjects when viewing video sequences freely. Moreover the papers compares the eye positions of subjects with interest maps obtained using two classical interest point detectors: one spatial and one space-time. We fund that in function of the video sequence, and more especially in function of the motion inside the sequence, the spatial or the space-time interest point detector is more or less relevant to predict eye movements.

To cite this version:Alain Simac-Lejeune. Moving object analysis in video sequences using space-time interest points. Abstract: Among all the features which can be extracted from videos, we propose to use Space-Time Interest Points (STIPs). STIPs are particularly interesting because they are simple and robust low-level features providing an efficient characterization of moving objects within videos. In this paper, after defining STIPs and after giving some of their properties, we will use STIPs to detect moving objects and to characterize specific changes in the movements of these objects. Proposed results are obtained from two very different types of videos, namely athletic videos and animation movies. MOVING OBJECT ANALYSIS IN VIDEO SEQUENCES USING SPACE-TIME INTEREST POINTS

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