Track Initialization in Low Frame Rate and Low Resolution Videos

Abstract

The problem of object detection and tracking has received relatively less attention in low frame rate and low resolution videos. Here we focus on motion segmentation in videos where objects appear small (less than 30-pixel tall people) and have low frame rate (less than 5 Hz). We study challenging cases where some of the, otherwise successful, approaches may break down. We investigate a number of popular techniques in computer vision that have been shown to be useful for discriminating various spatio-temporal signatures. These include: Histogram of oriented Gradients (HOG), Histogram of oriented optical Flow (HOF) and Haar-features (Viola and Jones). We use these feature to classify the motion segmentations into person vs. other and vehicle vs. other. We rely on aligned motion history images to create a more consistent object representation across frames. We present results on these features using webcam data and wide-area aerial video sequences.