Consolidation of Symbolic Instances Using Sensor Data via Tracklet Merging for Long-Term Monitoring of Crops
Abstract
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%.
Cite
Text
Niemeyer et al. "Consolidation of Symbolic Instances Using Sensor Data via Tracklet Merging for Long-Term Monitoring of Crops." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-91835-3_10Markdown
[Niemeyer et al. "Consolidation of Symbolic Instances Using Sensor Data via Tracklet Merging for Long-Term Monitoring of Crops." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/niemeyer2024eccvw-consolidation/) doi:10.1007/978-3-031-91835-3_10BibTeX
@inproceedings{niemeyer2024eccvw-consolidation,
title = {{Consolidation of Symbolic Instances Using Sensor Data via Tracklet Merging for Long-Term Monitoring of Crops}},
author = {Niemeyer, Mark and Hertzberg, Joachim and Cielniak, Grzegorz},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {146-159},
doi = {10.1007/978-3-031-91835-3_10},
url = {https://mlanthology.org/eccvw/2024/niemeyer2024eccvw-consolidation/}
}