A new multi-object tracking pipeline based on computer vision techniques for mussel farms

Mussel farming is a thriving industry in New Zealand and is crucial to local communities. Currently, farmers keep track of their mussel floats by taking regular boat trips to the farm. This is a labour-intensive assignment. Integrating computer vision techniques into aquafarms will significantly alleviate the pressure on mussel farmers. However, tracking a large number of identical targets under various image conditions raises a considerable challenge. This paper proposes a new computer vision-based pipeline to automatically detect and track mussel floats in images. The proposed pipeline consists of three steps, i.e. float detection, float description, and float matching. In the first step, a new detector based on several image processing operators is used to detect mussel floats of all sizes in the images. Then a new descriptor is employed to provide unique identity markers to mussel floats based on the relative positions of their neighbours. Finally, float matching across adjacent frames is done by image registration. Experimental results on the images taken in Marlborough Sounds New Zealand have shown that the proposed pipeline achieves an 82.9% MOTA - 18% higher than current deep learning-based approaches - without the need for training.