M2S2: A Multimodal Sensor System for Remote Animal Motion Capture in the Wild

Capturing animal locomotion in the wild is far more challenging than in controlled laboratory settings. Wildlife subjects move unpredictably, and issues, such as scaling, occlusion, lighting changes, and the lack of ground truth data, make motion capture difficult. Unlike human biomechanics, where m...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE sensors letters Jg. 9; H. 4; S. 1 - 4
Hauptverfasser: Vally, Azraa, Maswoswere, Gerald, Bowden, Nicholas, Paine, Stephen, Amayo, Paul, Markham, Andrew, Patel, Amir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2025
Schlagworte:
Animals
Calibration
Cameras
Laser radar
Motion capture
Pose estimation
Radar
sensor fusion
Sensor systems
Sensors
Synchronization
Thermal sensors
ISSN:2475-1472, 2475-1472
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Capturing animal locomotion in the wild is far more challenging than in controlled laboratory settings. Wildlife subjects move unpredictably, and issues, such as scaling, occlusion, lighting changes, and the lack of ground truth data, make motion capture difficult. Unlike human biomechanics, where machine learning thrives with annotated datasets, such resources are scarce for wildlife. Multimodal sensing offers a solution by combining the strengths of various sensors, such as Light Detection and Ranging {LiDAR) and thermal cameras, to compensate for individual sensor limitations. In addition, some sensors, like LiDAR, can provide training data for monocular pose estimation models. We introduce a multimodal sensor system (M2S2) for capturing animal motion in the wild. M2S2 integrates RGB, depth, thermal, event, LiDAR, and acoustic sensors to overcome challenges like synchronization and calibration. We showcase its application with data from cheetahs, offering a new resource for advancing sensor fusion algorithms in wildlife motion capture.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2025.3542233