Location-matching tracking under convolutional neural network
Daqian Liu, Wanjun Liu, Bowen Fei · Journal of Electronic Imaging · 2018
Traditional trackers are easily affected by uncertain changes in tracking targets, such as occlusion, deformation, and background clutter. To solve these problems, we propose a tracking method, namely location-matching tracking under a convolutional neural network (CNN), which consists of a process of localization, recognition, and model updating. In the location subprocess, the target’s locations of the previous (first) frame and the current frame are utilized to estimate a series of specific regions by the average displacement, and these locations are proven to be useful to improve the probability of a successful tracking. In the recognition subprocess, a CNN is adopted to classify the estimated regions, and we calculate the confidence score maps of these regions to estimate the final target region. To improve the accuracy of the tracking, we propose an optimal similarity matching to verify the final target region and make a confidence decision to update the network. Compared with the state-of-the-art trackers on challenging object tracking benchmark benchmarks, the proposed method can achieve the same or even higher tracking accuracy.