Object detection based on Multi-viewpoint histogram

Object detection is a key issue in computer vision, and technologies based local descriptor become more and more mature, especially in pedestrian detection. In this paper, we present a novel algorithm to detect targets from an image. Local descriptor has been used in object detection for quite some time and proven to be a valuable tool. However, Local descriptors are usually accompanied by boundary effect and poor continuity. Our algorithm addresses these problems via utilizing the global descriptor, called Multi-viewpoint histogram (MVH), which is more likely to rotate and zoom. The challenge of using just the global descriptor is what can not provide enough global information and local information. Different from the previous researches, this study integrates sufficient global information and local information of image by multi-viewpoints and probability density function (PDF). The experimental results demonstrate that our algorithm has fared well in some publically available database, moreover, outperforms current state-of-the-art methods in some respects, such as image matching between different spectra images and in the case of the small targets.