- author = "Dylon Zeng and Ivy Liu and Ying Bi and Ross Vennell and Dana Briscoe and Bing Xue and Mengjie Zhang",
- title = "A new multi-object tracking pipeline based on computer vision techniques for mussel farms",
- journal = "Journal of the Royal Society of New Zealand",
- year = "2025",
- volume = "55",
- number = "1",
- pages = "62--81",
- keywords = "genetic algorithms, genetic programming, Object tracking, computer vision, object detection, object description, aquaculture, mussel farming, Marlborough Sounds New Zealand",
- publisher = "Taylor \& Francis",
-
DOI = "
doi:10.1080/03036758.2023.2240466",
- size = "20 pages",
- abstract = "... 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 percent MOTA 18percent higher than current deep learning-based approaches without the need for training.",
-
notes = "See \cite{Zeng:2023:IVCNZ}
Published online: 30 Jul 2023
",
