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Institute of Animal Welfare, Animal Behavior, and Laboratory Animal Science, Department of Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany

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1 Papers

Upper Bound Tracker: A Multi-Animal Tracking Solution for Closed Laboratory Settings

Topics: Object Detection and Localization; Tracking and Visual Navigation; Deep Learning for Tracking

Authors: Alexander Dolokov ¹ , Niek Andresen ^{1

;

2} , Katharina Hohlbaum ³ , Christa Thöne-Reineke ^{4

;

2} , Lars Lewejohann ^{4

;

2

;

3} and Olaf Hellwich ^{1

;

2}

Affiliations: ¹ Department of Computer Vision & Remote Sensing, Technische Universität Berlin, 10587 Berlin, Germany , ² Science of Intelligence, Research Cluster of Excellence, Marchstr. 23, 10587 Berlin, Germany , ³ German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany , ⁴ Institute of Animal Welfare, Animal Behavior, and Laboratory Animal Science, Department of Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany

Digital Object Identifier: 10.5220/0011609500003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

When tracking multiple identical objects or animals in video, many erroneous results are implausible right away, because they ignore a fundamental truth about the scene. Often the number of visible targets is bounded. This work introduces a multiple object pose estimation solution for the case that this upper bound is known. It dismisses all detections that would exceed the maximally permitted number and is able to re-identify an individual after an extended period of occlusion including the re-appearance in a different place. An example dataset with four freely interacting laboratory mice is additionally introduced and the tracker’s performance demonstrated on it. The dataset contains various conditions ranging from almost no opportunity to hide for the mice to a fairly cluttered environment. The approach is able to significantly reduce the occurrences of identity switches - the error when a known individual is suddenly identified as a different one - compared to other current solutions.