Flipflop correlation tracking with Convolution Kernels Networks

Abstract

Correlation filter-based tracking methods have accomplished competitive performance on accuracy and robustness, but there is still a huge potential in choosing suitable features. Recently, Convolutional Kernel Networks (CKN), which provide a fast and simple procedure to approximate kernel descriptors, have been proposed and achieved state-of-the-art performance in many vision tasks. In this paper, we present an adaptive tracker which integrates the kernel correlation filters with multiple effective CKN descriptors. By adopting a FlipFlop scheme, the weights of different features can be adjusted in the process of tracking to get better performance. Extensive experimental results on the OTB-2013 tracking benchmark show that our approach performs favorably against some representative state-of-the-art tracking algorithms.