Human detection in fish-eye images using HOG-based detectors over rotated windows

Abstract

Fish-eye cameras are efficient means to provide an omni-view video recording over a large area using a single camera. Although effective algorithms for human detection in images captured by conventional cameras have been developed, human detection in fish-eye images remains an open challenge. Recognizing that humans typically appear on radial lines emitted from the center in fish-eye images, we propose to apply the popular human detection algorithm based on the Histogram of Oriented Gradient (HOG) features after rotating each search window on a radial line to the vertical reference line. We extract positive and negative examples by such rotations to train the SVM classifier using HOG features. To detect humans in a given image, we rotate the image successively and detect windows containing humans along the reference line after each rotation using the trained classifier. We use multiple window sizes to detect people with different appearance sizes. We further develop an algorithm to discover multiple overlapping windows covering the same person and identify the window that encloses the person the best. The proposed method has yielded highly accurate human detection in low-resolution, low-contrast images containing multiple people with varying poses and sizes.