Difference between revisions of "Object Movement Prediction for Autonomous Cars"
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|Summary=Predicting the movement of objects in the context of autonomous cars | |Summary=Predicting the movement of objects in the context of autonomous cars | ||
|References=https://motchallenge.net | |References=https://motchallenge.net | ||
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https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection | https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection | ||
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| + | https://arxiv.org/pdf/1909.07707.pdf | ||
|Supervisor=Tiago Cortinhal | |Supervisor=Tiago Cortinhal | ||
|Level=Master | |Level=Master | ||
|Status=Open | |Status=Open | ||
}} | }} | ||
| − | + | Nowadays, we have several powerful architectures, e.g. YOLO, that allows us to find bounding boxes on the fly. | |
| − | + | ||
| + | |||
| + | Single-object tracking focus on the processing of sequences of RGB images to be able to identify and track a given object, which can be costly in terms of memory/computation. The main idea being this project is to use the bounding boxes itself and try to predict its movement based on the n-previous frames. By using this higher-level abstraction of the scene itself we might reduce the complexity and training time required for traditional Single-Object tracking. | ||
| + | |||
| + | To start, we can use Kitti dataset to create such a prediction system and exploit other possible datasets/possible settings as soon as we have a working prototype. | ||
Latest revision as of 15:59, 3 October 2019
| Title | Object Movement Prediction for Autonomous Cars |
|---|---|
| Summary | Predicting the movement of objects in the context of autonomous cars |
| Keywords | |
| TimeFrame | |
| References | https://motchallenge.net
https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection |
| Prerequisites | |
| Author | |
| Supervisor | Tiago Cortinhal |
| Level | Master |
| Status | Open |
Nowadays, we have several powerful architectures, e.g. YOLO, that allows us to find bounding boxes on the fly.
Single-object tracking focus on the processing of sequences of RGB images to be able to identify and track a given object, which can be costly in terms of memory/computation. The main idea being this project is to use the bounding boxes itself and try to predict its movement based on the n-previous frames. By using this higher-level abstraction of the scene itself we might reduce the complexity and training time required for traditional Single-Object tracking.
To start, we can use Kitti dataset to create such a prediction system and exploit other possible datasets/possible settings as soon as we have a working prototype.
