Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments

Citation

Baisa NL, Bhowmik D & Wallace A (2018) Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments. Journal of Visual Communication and Image Representation, 55, pp. 464-476. https://doi.org/10.1016/j.jvcir.2018.06.027

Abstract
Tracking a target of interest in both sparse and crowded environments is a challenging problem, not yet successfully addressed in the literature. In this paper, we propose a new long-term visual tracking algorithm, learning discriminative correlation filters and using an online classifier, to track a target of interest in both sparse and crowded video sequences. First, we learn a translation correlation filter using a multi-layer hybrid of convolutional neural networks (CNN) and traditional hand-crafted features. Second, we include a re-detection module for overcoming tracking failures due to long-term occlusions using online SVM and Gaussian mixture probability hypothesis density (GM-PHD) filter. Finally, we learn a scale correlation filter for estimating the scale of a target by constructing a target pyramid around the estimated or re-detected position using the HOG features. We carry out extensive experiments on both sparse and dense data sets which show that our method significantly outperforms state-of-the-art methods.

Keywords
Media Technology; Signal Processing; Electrical and Electronic Engineering; Computer Vision and Pattern Recognition

Journal
Journal of Visual Communication and Image Representation: Volume 55

• LCMHT-JVCI