A fast online incremental learning method for object detection and pose classification using voting and combined appearance modeling

This paper presents a novel and rapid object detection method that identifies object positions and classifies object views. To overcome the limitations of appearance-based object recognition, we integrate a spatial relationship between local key points and object center position. A voting technique is applied to estimate the object area and then construct a bounding box to capture the object. A combined appearance model is introduced by a recall image to help deal with false detection problems. Experimental results show that our method can improve the object detection time while still preserving the average precision results. Moreover, our method can improve the accuracy of view classification.

► We propose a fast online incremental learning method for object detection.
► Our method can classify object poses without using any geometric or 3D models.
► We propose new confidence score based on our proposed combined appearance modeling.
► Our method can work with limited number of object views.
► Experiment results show that our method can reduce the detection time.