Publikation
Consolidation of symbolic instances using sensor data via tracklet merging for long-term monitoring of crops
Mark Niemeyer; Joachim Hertzberg; Grzegorz Cielniak
In: Alessio Del Bue; Cristian Canton; Jordi Pont-Tuset; Tatiana Tommasi (Hrsg.). Computer Vision - ECCV 2024 Workshops. Computer Vision in Plant Phenotyping and Agriculture (CVPPA-2024), 9th Computer Vision in Plant Phenotyping and Agriculture, located at 18th European Conference on Computer Vision ECCV 2024, September 29-30, Milano, Italy, Pages 146-159, LNCS, Vol. 15625, ISBN 978-3-031-91835-3, Springer, Cham, 5/2025.
Zusammenfassung
Long-term monitoring of plants is crucial for many practical applications in agriculture. This task requires associating the sensor data over long timeframes to a single individual object representing a unique symbolic instance. In general, this is a well-known tracking problem, but state-of-the-art trackers cannot track objects over their whole lifetime reliably, due to occlusions and changes in the object's appearance, thus only sub-tracks / tracklets are created. This paper proposes a methodology to consolidate the tracklets into the real track. For the consolidation of two tracklets matching costs using spatial, temporal and appearance-based information are calculated and the overall matching of the tracklets is optimised using the Hungarian algorithm. Using a strawberry tracking dataset for the evaluation, the presented methodology shows promising results and can match the tracklets with an accuracy of 72.5 %.