Active vision: on the relevance of a bio-inspired approach for object detection

Starting from biological systems, we review the interest of active perception for object recognition in an autonomous system. Foveated vision and control of the eye saccade introduce strong benefits related to the differentiation of a 'what' pathway recognizing some local parts in the image and a 'where' pathway related to moving the fovea in that part of the image. Experiments on a dataset illustrate the capability of our model to deal with complex visual scenes. The results enlighten the interest of top-down contextual information to serialize the exploration and to perform some kind of hypothesis test. Moreover learning to control the occular saccade from the previous one can help reducing the exploration area and improve the recognition performances. Yet our results show that the selection of the next saccade should take into account broader statistical information. This opens new avenues for the control of the ocular saccades and the active exploration of complex visual scenes.