Re-Identifying Kākā with AI-Automated Video Key Frame Extraction

• URL: http://arxiv.org/abs/2510.08775v1
• Date: Thu, 09 Oct 2025 19:46:46 GMT
• Title: Re-Identifying Kākā with AI-Automated Video Key Frame Extraction
• Authors: Paula Maddigan, Andrew Lensen, Rachael C. Shaw,
• Abstract summary: This study presents a unique pipeline for extracting high-quality key frames from videos of k=ak=a (Nestor meridionalis)<n>Using video recordings at a custom-built feeder, we extract key frames and evaluate the re-identification performance of our pipeline.<n>Results indicate that our proposed key frame selection methods yield image collections which achieve high accuracy in k=ak=a re-identification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate recognition and re-identification of individual animals is essential for successful wildlife population monitoring. Traditional methods, such as leg banding of birds, are time consuming and invasive. Recent progress in artificial intelligence, particularly computer vision, offers encouraging solutions for smart conservation and efficient automation. This study presents a unique pipeline for extracting high-quality key frames from videos of k\={a}k\={a} (Nestor meridionalis), a threatened forest-dwelling parrot in New Zealand. Key frame extraction is well-studied in person re-identification, however, its application to wildlife is limited. Using video recordings at a custom-built feeder, we extract key frames and evaluate the re-identification performance of our pipeline. Our unsupervised methodology combines object detection using YOLO and Grounding DINO, optical flow blur detection, image encoding with DINOv2, and clustering methods to identify representative key frames. The results indicate that our proposed key frame selection methods yield image collections which achieve high accuracy in k\={a}k\={a} re-identification, providing a foundation for future research using media collected in more diverse and challenging environments. Through the use of artificial intelligence and computer vision, our non-invasive and efficient approach provides a valuable alternative to traditional physical tagging methods for recognising k\={a}k\={a} individuals and therefore improving the monitoring of populations. This research contributes to developing fresh approaches in wildlife monitoring, with applications in ecology and conservation biology.

Related papers

• Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection [51.93878677594561]
  In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data.<n>We propose a Policy-Guided Outlier Synthesis framework that replaces statics with a learned exploration strategy.
  arXiv  Detail & Related papers  (2026-02-28T11:40:18Z)
• Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring [36.85680419418593]
  We propose running machine learning models on inexpensive microcontroller units (MCUs) directly in the field.<n>We trained and compressed models for various numbers of target classes to assess the detection of multiple bird species.<n>We also provide benchmarking results for different hardware platforms and evaluate the feasibility of deploying energy-autonomous devices.
  arXiv  Detail & Related papers  (2026-02-19T16:24:33Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• Consistent multi-animal pose estimation in cattle using dynamic Kalman filter based tracking [0.0]
  KeySORT is an adaptive Kalman filter to construct tracklets in a bounding-box free manner, significantly improving the temporal consistency of detected keypoints.<n>Our test results indicate our algorithm is able to detect up to 80% of the ground truth keypoints with high accuracy.
  arXiv  Detail & Related papers  (2025-03-13T15:15:54Z)
• Categorical Keypoint Positional Embedding for Robust Animal Re-Identification [22.979350771097966]
  Animal re-identification (ReID) has become an indispensable tool in ecological research.<n>Unlike human ReID, animal ReID faces significant challenges due to the high variability in animal poses, diverse environmental conditions, and the inability to directly apply pre-trained models to animal data.<n>This work introduces an innovative keypoint propagation mechanism, which utilizes a single annotated pre-trained diffusion model.
  arXiv  Detail & Related papers  (2024-12-01T14:09:00Z)
• Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data [0.06819010383838325]
  Camera traps offer enormous new opportunities in ecological studies. Current automated image analysis methods often lack contextual richness needed to support impactful conservation outcomes. Here we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
  arXiv  Detail & Related papers  (2024-11-21T15:28:52Z)
• Image-Based Leopard Seal Recognition: Approaches and Challenges in Current Automated Systems [0.0]
  This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography. We used the leopard seal, emphHydrurga leptonyx, a key species within Antarctic ecosystems, to review the different available methods found. The advent of machine learning, particularly through the application of vision transformers, heralds a new era of efficiency and precision in species monitoring.
  arXiv  Detail & Related papers  (2024-08-14T03:35:11Z)
• Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images [57.96659470133514]
  Motion-activated camera traps constitute an efficient tool for tracking and monitoring wildlife populations across the globe. Supervised learning techniques have been successfully deployed to analyze such imagery, however training such techniques requires annotations from experts. Reducing the reliance on costly labelled data has immense potential in developing large-scale wildlife tracking solutions with markedly less human labor.
  arXiv  Detail & Related papers  (2023-11-02T08:32:00Z)
• Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching [74.75284453828017]
  Open-Vocabulary Keypoint Detection (OVKD) task is innovatively designed to use text prompts for identifying arbitrary keypoints across any species. We have developed a novel framework named Open-Vocabulary Keypoint Detection with Semantic-feature Matching (KDSM) This framework combines vision and language models, creating an interplay between language features and local keypoint visual features.
  arXiv  Detail & Related papers  (2023-10-08T07:42:41Z)
• AntPivot: Livestream Highlight Detection via Hierarchical Attention Mechanism [64.70568612993416]
  We formulate a new task Livestream Highlight Detection, discuss and analyze the difficulties listed above and propose a novel architecture AntPivot to solve this problem. We construct a fully-annotated dataset AntHighlight to instantiate this task and evaluate the performance of our model.
  arXiv  Detail & Related papers  (2022-06-10T05:58:11Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• Automatic Detection and Recognition of Individuals in Patterned Species [4.163860911052052]
  We develop a framework for automatic detection and recognition of individuals in different patterned species. We use the recently proposed Faster-RCNN object detection framework to efficiently detect animals in images. We evaluate our recognition system on zebra and jaguar images to show generalization to other patterned species.
  arXiv  Detail & Related papers  (2020-05-06T15:29:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.