Category Attentional Search for Fast Object Detection by Mimicking Human Visual Perception
In this paper, we propose a novel selective search method to speed up the object detection via category-based attention scheme. The proposed attentional searching strategy is designed to focus on a small set of selected regions where the object category is expected to exist. The selected regions are estimated by mimicking three properties of the attentional scheme of human visual perception: spotlighting interest regions with low-level saliency (saliency attention), focusing on distinctive features for an object category (feature attention), and estimating potential object position by following human gaze path (gaze attention).
Also, the time complexity of each attentional scheme is implemented to be low so that it can hardly affect the computational time. To validate the performance of our method, experiments were conducted on the challenging PASCAL VOC dataset. Experimental results show that our method efficiently generates a small number of candidate boxes for object detection (less than 10ms=image), and the combined object detection system achieves more than 2 times faster performance than the baseline with comparable average precision.