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An ART-type network approach for video object detection

Abstract:

This paper presents an ART-type network (adaptive resonant theory) to detect objects in a video sequence classifying the pixels as foreground or background. The proposed ART network (ART+) not only possesses the structure and learning ability of an ART-based network, but also uses a neural merging process to adapt the variability of the input data (pixels) in the scene along the time. Experimental results demonstrate the effectiveness and feasibility of the proposed ART+ approach for object detection. Standard datasets are used to compare the efficiency of the proposed approach against other traditional methods based on gaussian models.