ATRW: A Benchmark for Amur Tiger Re-identification in the Wild

Abstract

Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while applying computer vision methods to camera sensor data requires re-identification (re-ID) algorithms to obtain accurate counts and moving trajectory of wildlife. However, existing re-ID methods are largely targeted at persons and cars, which have limited pose variations and constrained capture environments. This paper tries to fill the gap by introducing a novel large-scale dataset, the Amur Tiger Re-identification in the Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. In contrast to typical re-ID datasets, the tigers are captured in a diverse set of unconstrained poses and lighting conditions. We demonstrate with a set of baseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, we propose a novel method for tiger re-identification, which introduces precise pose parts modeling in deep neural networks to handle large pose variation of tigers, and reaches notable performance improvement over existing re-ID methods. The dataset is public available at https://cvwc2019.github.io/ .

arXiv commentACM Multimedia (MM) 2020

References in corpus (12)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• YOLOv3: An Incremental Improvement
• In Defense of the Triplet Loss for Person Re-Identification
• Improving Person Re-identification by Attribute and Identity Learning
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
• Deep Transfer Learning for Person Re-identification
• Pose Invariant Embedding for Deep Person Re-identification
• Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
• SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
• Towards Automatic Identification of Elephants in the Wild
• Right whale recognition using convolutional neural networks

Cited by in corpus (6)

• Seeing biodiversity: perspectives in machine learning for wildlife conservation
• Perspectives on individual animal identification from biology and computer vision
• AP-10K: A Benchmark for Animal Pose Estimation in the Wild
• Part-Guided Attention Learning for Vehicle Instance Retrieval
• Low-power, Continuous Remote Behavioral Localization with Event Cameras
• A Novel Dataset for Keypoint Detection of quadruped Animals from Images