Camouflaged Object Tracking: A Benchmark

TL;DR

This paper introduces a new benchmark dataset and a tracking framework specifically designed for camouflaged objects, highlighting the challenges and proposing solutions for improved detection and tracking in complex environments.

Contribution

The paper presents the Camouflaged Object Tracking Dataset (COTD) and a novel tracking framework, HiPTrack-MLS, addressing the gap in tracking camouflaged objects.

Findings

Existing algorithms perform poorly on camouflaged objects.

COTD dataset contains 200 sequences with detailed annotations.

HiPTrack-MLS improves tracking accuracy for camouflaged objects.

Abstract

Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. This challenge is particularly critical in applications such as military, security, agriculture, and marine monitoring, where precise tracking of camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://github.com/openat25/HIPTrack-MLS.