[10%] Object Recognition: using YOLOv4, we used the weights from the model, and created a YoloNet, which is trained as a convolutional neural network that detects objects within a 2D image.
[30%] Performance Optimization: we developed the ObjectSet data structure, that iterates over the frames of a video, and on each frame, incorporates more information. The ObjectSet holds a list of PointSets, and each PointSet represents a collection of 3D points that constitute an object within the real world. At each iteration, we find new candidate objects from the new frame, and iteratively check if these new objects are instances of previously found PointSets, or previously undiscovered objects within the real world.
-[10%] Object Recognition: using YOLOv4, we used the weights from the model, and created a YoloNet, which is trained as a convolutional neural network that detects objects within a 2D image.
-[30%] Performance Optimization: we developed the ObjectSet data structure, that iterates over the frames of a video, and on each frame, incorporates more information. The ObjectSet holds a list of PointSets, and each PointSet represents a collection of 3D points that constitute an object within the real world. At each iteration, we find new candidate objects from the new frame, and iteratively check if these new objects are instances of previously found PointSets, or previously undiscovered objects within the real world.
> For this feature, we have not fully optimized it, and we plan on improving in before the final report
[15%] Integration with EuRoC MAV and other Datasets:
-[15%] Integration with EuRoC MAV and other Datasets:
> This feature has NOT been implemented, but we plan on building this before the final report
[30%] Object Tracking: object tracking occurs when we iterate over each KeyFrame, placing objects into the objectset.
-[30%] Object Tracking: object tracking occurs when we iterate over each KeyFrame, placing objects into the objectset.
[15%] Comprehensive Benchmark:
> This feature has NOT been implemented, but we plan on building this before the final report
[10%] Server and Database: we implemented the server with Spring-Boot, and wrote a GUI in JavaScript/CSS/HTML that is served by a Java backend. This makes it easy to view the pointcloud view of the room, and choose objects to be highlighted on the display.
-[10%] Server and Database: we implemented the server with Spring-Boot, and wrote a GUI in JavaScript/CSS/HTML that is served by a Java backend. This makes it easy to view the pointcloud view of the room, and choose objects to be highlighted on the display.
