Action Recognition with a Bio-inspired Feedforward Motion Processing Model: The Richness of Center-Surround Interactions

Here we show that reproducing the functional properties of MT cells with various center---surround interactions enriches motion representation and improves the action recognition performance. To do so, we propose a simplified bio---inspired model of the motion pathway in primates: It is a feedforward model restricted to V1-MT cortical layers, cortical cells cover the visual space with a foveated structure and, more importantly, we reproduce some of the richness of center-surround interactions of MT cells. Interestingly, as observed in neurophysiology, our MT cells not only behave like simple velocity detectors, but also respond to several kinds of motion contrasts. Results show that this diversity of motion representation at the MT level is a major advantage for an action recognition task. Defining motion maps as our feature vectors, we used a standard classification method on the Weizmann database: We obtained an average recognition rate of 98.9%, which is superior to the recent results by Jhuang et al. (2007). These promising results encourage us to further develop bio---inspired models incorporating other brain mechanisms and cortical layers in order to deal with more complex videos.