TrichTrack: Multi-Object Tracking of Small-Scale Trichogramma Wasps

Vishal Pani (1, 2) , Martin Bernet (3) , Vincent Calcagno (4) , Louise van Oudenhove (4) , François F Bremond (2)

1 IIIT Allahabad - Indian Institute of Information Technology Allahabad
2 STARS - Spatio-Temporal Activity Recognition Systems
3 INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
4 ISA - Institut Sophia Agrobiotech

Trichogramma wasps behaviors are studied extensively due to their effectiveness as biological control agents across the globe. However, to our knowledge, the field of intra/inter-species Trichogramma behavior is yet to be explored thoroughly. To study these behaviors it is crucial to identify and track Trichogramma individuals over a long period in a lab setup. For this, we propose a robust tracking pipeline named TrichTrack. Due to the unavailability of labeled data, we train our detector using an iterative weakly supervised method. We also use a weakly supervised method to train a Re-Identification (ReID) network by leveraging noisy tracklet sampling. This enables us to distinguish Trichogramma individuals that are indistinguishable from human eyes. We also develop a two-staged tracking module that filters out the easy association to improve its efficiency. Our method outperforms existing insect trackers on most of the MOTMetrics, specifically on ID switches and fragmentations.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Biologie animale

Vishal Pani, Martin Bernet, Vincent Calcagno, Louise van Oudenhove, François F Bremond. TrichTrack: Multi-Object Tracking of Small-Scale Trichogramma Wasps. AVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-based Surveillance, Nov 2021, Virtual, United States. ⟨10.1109/AVSS52988.2021.9663814⟩. ⟨hal-03555579⟩