Roboflow Universe kaggleMTID Drone-Traffic

Drone-Traffic Computer Vision Project

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Brief Research-SFFP-2022:

Multimodal machine learning aims to build models that can process and relate information from different modalities such as EO, IR, Hyperspectral, and RF. In recent years, machine learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. Current and past funded work was on unsupervised learning using Autoencoders. The sensor research extends current algorithms using additional knowledge from aerial meta-data to visual information processing for object localization. The recent work focuses on aerial multi-target detection.

Below is a link to the current model performance:**** ![https://app.roboflow.com/kagglemtid/drone-traffic/images/x3IaLCG7lkmqbtDljHiL?jobStatus=assigned&annotationJob=IIqCXItndaRBJKtzoyxU]

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            drone-traffic_dataset,
                            title = { Drone-Traffic Dataset },
                            type = { Open Source Dataset },
                            author = { kaggleMTID },
                            howpublished = { \url{ https://universe.roboflow.com/kagglemtid/drone-traffic } },
                            url = { https://universe.roboflow.com/kagglemtid/drone-traffic },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { may },
                            note = { visited on 2024-05-14 },
                            }