MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Conventional object detection methods often use local features based on object shape, of which the HOG feature is typical. In recent years, Color Self-Similarity (CSS) has been proposed as a local feature that uses color information. CSS involves computing color similarity as a basis for deciding the sameness of objects, and thus represent a feature that is effective for object detection. It has also been reported that detection performance can be improved by using the CSS feature together with the HOG feature or other shape-based feature.We propose a Color Similarity-based HOG (CS-HOG) feature that is based on color similarity for object detecting shapes. The CS-HOG feature enables clarification of the object shape by using color similarity to calculate the degree of object sameness, thus achieving highly accurate object detection. Evaluation experiments show that the CS-HOG feature improves performance from 22.5% and 27.2% compared to the HOG feature and the CSS feature, and by 4.2% compared to the HOG feature and the CSS feature used together.