CIS 565 – Final Project Proposal                                             

GPU-Accelerated Logo Detection

Object detection is an important task in Computer Vision community. The goal of it is trying to enable computers to automatically detect semantic objects, like human faces, cars or barriers in the digital images or videos. There are a lot of applications for object detection such as digit image retrieval, visual tracking and automated vehicle systems. In computer vision tasks, many operations for images are inherently parallel in computer vision field, like applying a filter for each patch in an image, so we can utilize GPU to improve the performance, like GPU-accelerated face detection and pedestrian detection included in the OpenCV.

For my final project I propose to implement a GPU-Accelerated Logo Detection system by using Histogram of Oriented Gradients (HOG) as feature descriptor. For a given target logo, the Logo Detection system will find the possible locations of target in the image. The logos in images may have different scales or orientations as target logo. To match the target logo in the image, I will use HOG which is a very common feature descriptor in computer vision. For a specific image, we can divide it into a bunch of overlapping blocks with same size. HOG builds a histogram of gradient orientation for each block of image and measures local appearance of logo.

In order to find target logo with different scales and orientations, I will create an image stack for the target logo and build codebook in each scale and orientation. The codebook will store HOG features and offsets to center for each block in the target logo. For each possible scale or orientation, I will calculate HOG for the input image and get each block matched with minimum distance in the codebook. Each image block will have a weighted vote for the possible center according to the offsets. The logo centers in the input image would hopefully get a lot of votes in right scales or orientations. Running this logo detection algorithm on GPU would hopefully accelerate per-block voting operation and searching process for right scales and orientations in input images with large size.