Object detection for noncooperative targets using HOG-based proposals

In order to detect noncooperative objects with unknown structures, template based matching approaches are generally adopted. They rely on a large set of manually-selected templates and slide them over the image to determine the potential locations of objects. The process is exhaustive and computationally inefficient. In this paper, we propose a novel object detection algorithm using improved features of histogram of oriented gradients (HOG) to reduce the search region of potential objects regardless of their prior information. Firstly, we improve the HOG descriptor to make it more discriminative. The capability of detecting objects comes from positive and negative features of the training dataset. Then, the cascaded support vector machine is used to train the model, aiming at selecting proposals with higher scores at each scale and aspect ratio. Lastly, the best proposal over all scales is chosen as the object detection region. Further experiments demonstrate that our method improves favorably the detection rate on VOC 2007 and achieves satisfying performance in satellite bracket detection.