  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. We combine
advantages of both the lower convolutional layer which retains more spatial
details for precise localization and the higher convolutional layer which
encodes semantic information for handling appearance variations, and then
integrate these with histogram of oriented gradients (HOG) and color-naming
traditional features. Second, we include a re-detection module for overcoming
tracking failures due to long-term occlusions by training an incremental
(online) SVM on the most confident frames using hand-engineered features. This
re-detection module is activated only when the correlation response of the
object is below some pre-defined threshold. This generates high score detection
proposals which are temporally filtered using a Gaussian mixture probability
hypothesis density (GM-PHD) filter to find the detection proposal with the
maximum weight as the target state estimate by removing the other detection
proposals as clutter. 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.
