Color Search: An efficient region proposal generation method

Inspired by the process of searching for targets by the human eyes, our proposed target candidate region generation method focuses on the color information of the target. Current mainstream target detection algorithms often incorporate an Region Proposal Network (RPN) component for finding out the possible locations and sizes of targets, which facilitates the training and optimization of the backbone network. The RPN network uses more convolutional layers, which makes it impossible to be applied in embedded platforms that require high real-time performance and does not take full advantage of the target's a priori information. In this paper, We convert the image from RGB color model to HSV model and reduce dimensionality to form a color set. After quantizing the color set, we can obtain the Q-HSV of the image by which to find out the possible locations of the target efficiently. With this method, we can find out the possible areas of the target using only one RGB template of the target, and it is not affected by target viewpoint changes, shape changes, etc. Our approach can significantly reduce the image data to be processed by recognition and detection algorithms by more than 80%.