Biblio

In most correlation filter target tracking algorithms, poor accuracy in the tracking process for complex field images of the target and scale change problems. To address these issues, this paper proposes an algorithm of adaptive multi-feature fusion with scale change correlation filtering tracking. Our algorithm is based on the rapid and simple Kernel-Correlated Filtering(K CF) tracker, and achieves the complementarity among image features by fusing multiple features of Color Nmae(CN), Histogram of Oriented Gradient(HOG) and Local Binary Pattern(LBP) with weights adjusted by visual evaluation functions. The proposed algorithm introduces scale pooling and bilinear interpolation to adjust the target template size. Experiments on the OTB-2015 dataset of 100 video frames are compared with several trackers, and the precision and success ratio of our algorithm on complex scene tracking problems are 17.7% and 32.1 % respectively compared to the based-KCF.

Long-term tracking is one of the most challenging problems in computer vision. During long-term tracking, the target object may suffer from scale changes, illumination changes, heavy occlusions, out-of-view, etc. Most existing tracking methods fail to handle object invisibility, supposing that the object is always visible throughout the image sequence. In this paper, a novel long-term tracking method is proposed, which mainly addresses the problem of object invisibility. We combine a correlation filter based tracker with an online classifier, aiming to estimate the object state and re-detect the object after its invisibility. In addition, an adaptive updating scheme is proposed for the appearance model of the object considering both visible and invisible situations. Quantitative and qualitative evaluations prove that our algorithm outperforms the state-of-the-art methods on the 20 benchmark sequences with object invisibility. Furthermore, the proposed algorithm achieves competitive performance with the state-of-the-art trackers on Object Tracking Benchmark which covers various challenging aspects in object tracking.