# Long-term Correlation Tracking using Multi-layer Hybrid Features in Sparse and Dense Environments

## TL;DR

This paper introduces a long-term visual tracking algorithm that combines multi-layer hybrid features, an online classifier, and a re-detection module to effectively track targets in both sparse and crowded environments, outperforming existing methods.

## Contribution

The paper presents a novel long-term tracking approach integrating multi-layer CNN features, traditional features, and a re-detection mechanism with probabilistic filtering, advancing robustness in complex scenarios.

## Key findings

- Significantly outperforms state-of-the-art tracking methods on various datasets.
- Effectively handles occlusions and appearance changes through re-detection.
- Achieves high accuracy in both sparse and dense environments.

## 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. 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.