Fusion and Self-adaptation of Color and Gradient Based Models for Object Detection and Localization in Applications of Service Robots

This paper presents a novel approach of object detection and localization for service robots, which combines color-based and gradient-based detectors and automatically adapts the color model according to the variation of lighting conditions. Exploiting complementary visual features, the fusion of color-based and gradient-based detectors can achieve both robust detection and accurate localization. In real world environment, the color-based detection according to an offline-learned general model may fail. From a new linear color variation model proposed in this paper, our approach can generate a specific model for the target object in the image and achieve self-adaptation of color detector for robust detection and accurate localization. The experiments show that the proposed method can significantly increase the detection rate for target object in various real world environments.