Fast object detection based on selective visual attention

• Simulate selective visual attention to propose an object detection method.
• Analyze the use of saliency map in the object detection.
• Extract salient candidate area to reduce the searching area of object detection.

Selective visual attention plays an important role in human visual system. In real life, human visual system cannot handle all of the visual information captured by eyes on time. Selective visual attention filters the visual information and selects interesting one for further processing such as object detection. Inspired by this mechanism, we construct an object detection method which can speed up the object detection relative to the methods that search objects by using sliding window. This method firstly extracts saliency map from the origin image, and then gets the candidate detection area from the saliency map by adaptive thresholds. To detect object, we only need to search the candidate detection area with the deformable part model. Since the candidate detection area is much smaller than the whole image, we can speed up the object detection. We evaluate the detection performance of our approach on PASCAL 2008 dataset, INRIA person dataset and Caltech 101 dataset, and the results indicate that our method can speed up the detection without decline in detection accuracy.