Article Dans Une Revue Ecological Informatics Année : 2025

Background-invariant re-identification of dogs from camera-trap videos in non-controlled environments

Cyril Barrelet
Gérard Subsol

Résumé

This paper addresses the general problem of re-identification in natural conditions with multiple camera traps, poor video quality and small datasets. We focus on generalizable re-identification of dogs in cross-camera setups, adapting from short-term to long-term scenarios. Long-term re-identification across multiple cameras presents challenges due to variations in background, camera angles, and lighting conditions. While realistic, few animal re-identification methods are tested under such settings, mainly due to the lack of datasets and high complexity of annotation. Short-term datasets are often used to train re-identification networks, since they can be simply generated through web scraping algorithms. We introduce two publicly available datasets: the YT-BB-Dog, a short-term dataset with 2723 dogs from YouTube videos, and the Sibetan, a long-term dataset featuring 59 dogs recorded over 5 days and 12 cameras placed in Sibetan, Bali, Indonesia. Our goal is to use the YT-BB-Dog to train a feature extractor robust to covariate shifts, enabling better generalization in unknown domains. Our experiments revealed that state-of-the-art (SOTA) methods trained on the YT-BB-Dog are heavily influenced by background variations and perform poorly on complex scenarios like Sibetan. To address this, we propose Background Invariant Feature extractOR (BIFOR), a three-step method that leverages a novel mini-batch sampling technique with triplet loss and online hard mining. BIFOR achieves SOTA performance on Sibetan, improving rank-1 accuracy of the baseline by more than 9%. We also present a complete pipeline combining detection, tracking, and re-identification based on BIFOR.

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hal-05492220 , version 1 (04-02-2026)

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Eugenio Dias Ribeiro Neto, Cyril Barrelet, Marc Chaumont, Gérard Subsol, Muhammad Nur Faiz Mahfudz, et al.. Background-invariant re-identification of dogs from camera-trap videos in non-controlled environments. Ecological Informatics, 2025, 93, pp.103547. ⟨10.1016/j.ecoinf.2025.103547⟩. ⟨hal-05492220⟩
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