Abstract
Object tracking is a very important step in building an intelligent video monitoring system that can protect people’s lives and property. In recent years, although visual tracking has made great progress in terms of speed and accuracy, there are still few real-time high-precision tracking algorithms. Although discriminative correlation filters have excellent performance in tracking speed, there are deficiencies in handling fast motion. This leads to the inability to achieve long-term stable tracking results. The long-time tracking with discriminative correlation filter (LT-DCF) was proposed to solve these deficiencies. We use larger size detection image blocks and smaller size filters to increase the proportion of real samples to solve the boundary effects of fast motion. And we combine the histogram of oriented gradient (HOG) feature detection and scale-invariant feature transform (SIFT) key point detection to solve the obstacles caused by scale variations. The detector with deep feature flow is then incorporated into the tracker to detect key frames to improve tracking accuracy. This method has achieved more than 75% of the distance accuracy and 70% of the overlapping success rate on the VOT2015 and VOT2016 datasets, and the stable tracking video length can reach 6895 frames.
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
