MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

This paper proposes Relational HOG (R-HOG) features for object detection, and binary selection by using a wildcard “*” with Real AdaBoost. HOG features are effective for object detection, but their focus on local regions makes them high-dimensional features. To reduce the memory required for the HOG features, this paper proposes a new feature, R-HOG, which creates binary patterns from the HOG features extracted from two local regions. This approach enables the created binary patterns to reflect the relationships between local regions. Furthermore, we extend the RHOG features by shifting the gradient orientations. These shifted Relational HOG (SR-HOG) features make it possible to clarify the size relationships of the HOG features. However, since R-HOG and SR-HOG features contain binary values not needed for classification, we have added a process to the Real AdaBoost learning algorithm in which “*” permits either of the two binary values “0” and “1”, and so valid binary values can be selected. Evaluation experiment demonstrated that the SR-HOG features introducing “*” offers better detection performance than the conventional method (HOG feature) despite the reduced memory requirements.