Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary

• URL: http://arxiv.org/abs/2412.16511v1
• Date: Sat, 21 Dec 2024 07:20:57 GMT
• Title: Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary
• Authors: Keon Moradi, Ethan Haque, Jasmeen Kaur, Alexandra B. Bentz, Eli S. Bridge, Golnaz Habibi,
• Abstract summary: This paper presents a novel approach for robust 3D tracking of multiple birds in an outdoor aviary using a multi-camera system. Our method addresses the challenges of visually similar birds and their rapid movements by leveraging environmental landmarks for enhanced feature matching and 3D reconstruction. We also provide a large annotated dataset of 80 birds residing in four enclosures for 20 hours of footage which provides a rich testbed for researchers in computer vision, ornithologists, and ecologists.
• Score: 39.35431651202991
• License:
• Abstract: This paper presents a novel approach for robust 3D tracking of multiple birds in an outdoor aviary using a multi-camera system. Our method addresses the challenges of visually similar birds and their rapid movements by leveraging environmental landmarks for enhanced feature matching and 3D reconstruction. In our approach, outliers are rejected based on their nearest landmark. This enables precise 3D-modeling and simultaneous tracking of multiple birds. By utilizing environmental context, our approach significantly improves the differentiation between visually similar birds, a key obstacle in existing tracking systems. Experimental results demonstrate the effectiveness of our method, showing a $20\%$ elimination of outliers in the 3D reconstruction process, with a $97\%$ accuracy in matching. This remarkable accuracy in 3D modeling translates to robust and reliable tracking of multiple birds, even in challenging outdoor conditions. Our work not only advances the field of computer vision but also provides a valuable tool for studying bird behavior and movement patterns in natural settings. We also provide a large annotated dataset of 80 birds residing in four enclosures for 20 hours of footage which provides a rich testbed for researchers in computer vision, ornithologists, and ecologists. Code and the link to the dataset is available at https://github.com/airou-lab/3D_Multi_Bird_Tracking

Related papers

• Temporally-consistent 3D Reconstruction of Birds [5.787285686300833]
  We propose an approach to reconstruct the 3D pose and shape from monocular videos of a specific breed of seabird - the common murre. We provide a real-world dataset of 10000 frames of video observations on average capture nine birds simultaneously.
  arXiv  Detail & Related papers  (2024-08-24T17:12:36Z)
• A Flying Bird Object Detection Method for Surveillance Video [9.597393200515377]
  This paper proposes a Flying Bird Object Detection method for Surveillance Video (FBOD-SV) The FBOD-SV is validated using experimental datasets of flying bird objects in traction substation surveillance videos. The experimental results show that the FBOD-SV effectively improves the detection performance of flying bird objects in surveillance video.
  arXiv  Detail & Related papers  (2024-01-08T09:20:46Z)
• 3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking [14.52333427647304]
  We present 3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons at interactive speed using multiple camera views. For identity matching, we first dynamically match 2D detections to global identities in the first frame, then use a 2D tracker to maintain IDs across views in subsequent frames. We show that 3D-MuPPET also works in outdoors without additional annotations from natural environments.
  arXiv  Detail & Related papers  (2023-08-29T14:02:27Z)
• Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary [32.19504891200443]
  We introduce a system for studying the behavioral dynamics of a group of songbirds as they move throughout a 3D aviary. We study the complexities that arise when tracking a group of closely interacting animals in three dimensions and introduce a novel dataset for evaluating multi-view trackers.
  arXiv  Detail & Related papers  (2022-12-01T04:23:18Z)
• Aerial Monocular 3D Object Detection [67.20369963664314]
  DVDET is proposed to achieve aerial monocular 3D object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module. To encourage more researchers to investigate this area, we will release the dataset and related code.
  arXiv  Detail & Related papers  (2022-08-08T08:32:56Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild [51.35013619649463]
  We present an extensive dataset of free-running cheetahs in the wild, called AcinoSet. The dataset contains 119,490 frames of multi-view synchronized high-speed video footage, camera calibration files and 7,588 human-annotated frames. The resulting 3D trajectories, human-checked 3D ground truth, and an interactive tool to inspect the data is also provided.
  arXiv  Detail & Related papers  (2021-03-24T15:54:11Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [93.51773847125014]
  We propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. Our approach performs 3D convolutions across space and time over a bird's eye view representation of the 3D world.
  arXiv  Detail & Related papers  (2020-12-22T22:43:35Z)

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.