PlantTracing: Tracing Arabidopsis Thaliana Apex with CenterTrack
This work addresses a domain-specific problem in plant biology for researchers studying Arabidopsis Thaliana growth, but it is incremental as it applies an existing method to new data.
The paper tackled the problem of detecting and tracking the motion and growth of the Arabidopsis Thaliana flowering stem apex by applying an encoder-decoder-based machine learning network based on CenterTrack, achieving results tested on three videos after training on ten labeled time-lapsed videos.
This work applies an encoder-decoder-based machine learning network to detect and track the motion and growth of the flowering stem apex of Arabidopsis Thaliana. Based on the CenterTrack, a machine learning back-end network, we trained a model based on ten time-lapsed labeled videos and tested against three videos.