# Improved siamese tracking for temporal data association

## TL;DR

This paper introduces a more accurate and robust method for tracking objects over time using a Siamese network framework.

## Contribution

The novel contribution is an efficient temporal data association approach within the Siamese network framework for improved tracking performance.

## Key findings

- The proposed method achieves higher accuracy in temporal data association compared to standard Siamese trackers.
- Multi-tracker collaboration enhances robustness and outperforms state-of-the-art trackers.

## Abstract

Temporal image data association is essential for visual object tracking tasks. This association task is typically stated as a process of connecting signals from the same object at different times along the time axis. Temporal data association is usually performed before state estimation. The accuracy of data association processing results is fundamental to guaranteeing the correctness of all subsequent procedures. This paper proposes an efficient approach for temporal data association focused on obtaining accurate data association processing results in Siamese network framework. Siamese network has recently achieved strong power in visual object tracking owing to its balanced accuracy and speed. Based on data association processing and multi-tracker collaboration, our algorithm achieves high accuracy and strong robustness, which outperforms several state-of-the-art trackers, including standard Siamese trackers.