Planar Object Tracking in the Wild: A Benchmark

TL;DR

This paper introduces a new benchmark dataset of 210 videos capturing 30 planar objects in natural environments, designed to evaluate and advance planar object tracking algorithms under real-world conditions.

Contribution

It provides a comprehensive, high-quality benchmark with challenging scenarios for evaluating planar object tracking algorithms in the wild.

Findings

Evaluation of 11 algorithms on the benchmark.

Detailed analysis of algorithm performance.

Benchmark facilitates future research in real-world conditions.

Abstract

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.