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

Abstract

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.