This paper develops a HOG-based multistage approach for object detection and object pose recognition for service robots. This approach makes use of the merits of both multi-class and bi-class HOG-based detectors to form a three-stage algorithm at low computing cost. In the first stage, the multi-class classifier with coarse features is employed to estimate the orientation of a potential target object in the image; in the second stage, a bi-class detector corresponding to the detected orientation with intermediate level features is used to filter out most of false positives; and in the third stage, a bi-class detector corresponding to the detected orientation using fine features is used to achieve accurate detection with low rate of false positives. The training of multi-class and bi-class SVMs with their respective features in different levels is described. Experiments in real-world environments have shown that the proposed method is much more accurate than the detection method as it uses only multi-class detector. The proposed method is also much more efficient than the detection method as it uses a bi-class detector for each possible orientation. The approach works well on the scenarios where the SIFT-based detector may fail. The method can achieve real-time object detection, localization, and pose recognition on a P4 2.4GHz PC.
['Li Dong', 'Xinguo Yu', 'Liyuan Li', 'Jerry Kah Eng Hoe']
HOG based multi-stage object detection and pose recognition for service robot