# CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark

## TL;DR

This paper introduces CDTB, a comprehensive long-term visual object tracking dataset and benchmark with new performance measures, a taxonomy, and an integrated toolkit to advance long-term tracking research.

## Contribution

It presents a novel long-term tracking benchmark, new evaluation measures, a taxonomy, and an integrated toolkit, enabling better analysis and development of long-term trackers.

## Key findings

- New measures outperform existing ones in interpretation and distinguishing tracking behaviors.
- The benchmark evaluates the largest set of long-term trackers to date.
- Analysis of architecture, re-detection, and update strategies impacts on long-term performance.

## Abstract

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. We show that these measures generalize the short-term performance measures, thus linking the two tracking problems. Furthermore, the new measures are highly robust to temporal annotation sparsity and allow annotation of sequences hundreds of times longer than in the current datasets without increasing manual annotation labor. A new challenging dataset of carefully selected sequences with many target disappearances is proposed. A new tracking taxonomy is proposed to position trackers on the short-term/long-term spectrum. The benchmark contains an extensive evaluation of the largest number of long-term tackers and comparison to state-of-the-art short-term trackers. We analyze the influence of tracking architecture implementations to long-term performance and explore various re-detection strategies as well as influence of visual model update strategies to long-term tracking drift. The methodology is integrated in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate future development of long-term trackers.