Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos

• URL: http://arxiv.org/abs/2501.08931v1
• Date: Wed, 15 Jan 2025 16:34:20 GMT
• Title: Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos
• Authors: Javier Rodriguez-Juan, David Ortiz-Perez, Manuel Benavent-Lledo, David Mulero-Pérez, Pablo Ruiz-Ponce, Adrian Orihuela-Torres, Jose Garcia-Rodriguez, Esther Sebastián-González,
• Abstract summary: This study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification.<n>The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of datasets featuring videos of birds, and none of the existing datasets offer detailed annotations of bird behaviors in video format. In response to this gap, our study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification. This dataset addresses the need for comprehensive bird video datasets and provides detailed data on bird actions, facilitating the development of deep learning models to recognize these, similar to the advancements made in human action recognition. The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes. In addition, we also present baseline results using state of the art models on two tasks: bird behavior recognition and species classification.

Related papers

• Unsupervised outlier detection to improve bird audio dataset labels [0.0]
  Non-target bird species sounds can result in dataset labeling discrepancies referred to as label noise. We present a cleaning process consisting of audio preprocessing followed by dimensionality reduction and unsupervised outlier detection.
  arXiv  Detail & Related papers  (2025-04-25T19:04:40Z)
• MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps [41.58000025132071]
  MammAlps is a dataset of wildlife behavior monitoring from 9 camera-traps in the Swiss National Park. Based on 6135 single animal clips, we propose the first hierarchical and multimodal animal behavior recognition benchmark. We also propose a second ecology-oriented benchmark aiming at identifying activities, species, number of individuals and meteorological conditions.
  arXiv  Detail & Related papers  (2025-03-23T21:51:58Z)
• A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana [2.7924253850013416]
  We develop a pipeline for automatic bird vocalization identification in Donana National Park (SW Spain) We manually annotated 461 minutes of audio from three habitats across nine locations, yielding 3,749 annotations for 34 classes. Applying the Bird Song Detector before classification improved species identification, as all classification models performed better when analyzing only the segments where birds were detected.
  arXiv  Detail & Related papers  (2025-03-19T13:19:06Z)
• NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe [0.0]
  This work presents the Nocturnal Bird Migration dataset, a collection of 13,359 annotated vocalizations from 117 species of the Western Palearctic.<n>The dataset includes precise time and frequency annotations, gathered by dozens of bird enthusiasts across France.<n>We show that the accuracy of our recognition model on the 45 main species of the dataset competes with state-of-the-art systems trained on much larger datasets.
  arXiv  Detail & Related papers  (2024-12-04T18:55:45Z)
• Predicting Species Occurrence Patterns from Partial Observations [21.009271008147785]
  We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies. We propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found.
  arXiv  Detail & Related papers  (2024-03-26T18:29:39Z)
• Leveraging Habitat Information for Fine-grained Bird Identification [4.392299539811761]
  We are the first to explore integrating habitat information, one of the four major cues for identifying birds by ornithologists, into modern bird classifiers. We focus on two leading model types: CNNs and ViTs trained on the downstream bird datasets; and original, multi-modal CLIP. Training CNNs and ViTs with habitat-augmented data results in an improvement of up to +0.83 and +0.23 points on NABirds and CUB-200, respectively.
  arXiv  Detail & Related papers  (2023-12-22T16:23:22Z)
• 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)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping [22.30038765017189]
  We propose a metadata-aware self-supervised learning(SSL) framework useful for fine-grained classification and ecological mapping of bird species around the world. Our framework unifies two SSL strategies: Contrastive Learning(CL) and Masked Image Modeling(MIM), while also enriching the embedding space with metadata available with ground-level imagery of birds. We demonstrate that our models learn fine-grained and geographically conditioned features of birds, by evaluating on two downstream tasks: fine-grained visual classification(FGVC) and cross-modal retrieval.
  arXiv  Detail & Related papers  (2023-10-29T22:08:00Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• SuperAnimal pretrained pose estimation models for behavioral analysis [42.206265576708255]
  Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. We present a series of technical innovations that enable a new method, collectively called SuperAnimal, to develop unified foundation models.
  arXiv  Detail & Related papers  (2022-03-14T18:46:57Z)
• Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales [97.41394631426678]
  Recent research showed the promise of machine learning tools for analyzing acoustic communication in nonhuman species. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales. The technological capabilities developed are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research.
  arXiv  Detail & Related papers  (2021-04-17T18:39:22Z)
• Transferring Dense Pose to Proximal Animal Classes [83.84439508978126]
  We show that it is possible to transfer the knowledge existing in dense pose recognition for humans, as well as in more general object detectors and segmenters, to the problem of dense pose recognition in other classes. We do this by establishing a DensePose model for the new animal which is also geometrically aligned to humans. We also introduce two benchmark datasets labelled in the manner of DensePose for the class chimpanzee and use them to evaluate our approach.
  arXiv  Detail & Related papers  (2020-02-28T21:43:53Z)

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.