Object detection in low-resolution image via sparse representation

Abstract

We propose a novel object detection framework in extreme Low-Resolution (LR) images via sparse representation. Object detection in extreme LR images is very important for some specific applications such as abnormal event detection, automatic criminal investigation from surveillance videos. Object detection has achieved much progress in computer vision, but it is still a challenging task in LR image, because traditional discriminative features in high resolution usually disappear in low resolution. The precision of the detector in LR will decrease by a large margin. Our model uses sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. The main contribution of this paper: 1) the object detection framework in extreme LR is proposed by detecting objects in reconstructed HR image; 2) a mapping function from LR patches to High-Resolution (HR) patches will be learned by a local regression algorithm called sparse support regression, which can be constructed from the support based of the LR-HR dictionary; 3) a novel feature extraction method is proposed to accelerate by extracting visual features from HR dictionary atoms. Our approach has produced better performance for object detection than state-of-the-art methods. Testing our method from INRIA and PASCAL VOC 2007 datasets has revealed similar improvements, suggesting that our approach is suitable for general object detection applications.