Automatic Tracking Of Multiple Zebrafish Larvae With Resilience Against Segmentation Errors

The accurate tracking of zebrafish larvae movement is essential to many biomedical and neural science applications. This paper develops an accurate and reliable multiple zebrafish larvae tracking system resilient to detection and segmentation errors due to object misdetection and occlusion. The proposed system can therefore be applied to microscopic videos in unconstrained, realistic imaging conditions. Evaluated on a set of single and multiple adult and larvae zebrafish videos, a wide variety of (complex) video conditions were tested, including shadowing, labels, water bubbles and background artefacts. The proposed system obtains decreased overall MOTP error of up to 44.49 pixels compared to the commercial LoliTrack system, and increased MOTA accuracy by 31.57% compared with the state-of-the-art idTracker approach. The results offer an additional advantage of improved position detection, increased accuracy and unique identification compared to current techniques.