Video based real-time positional tracker
This addresses the problem of accurate real-time tracking for indoor or occluded objects, offering a practical alternative to GPS.
The paper tackles real-time object position tracking using video input from multiple cameras, achieving higher update rates and positioning precision than GPS-based systems, especially indoors or under occlusion.
We propose a system that uses video as the input to track the position of objects relative to their surrounding environment in real-time. The neural network employed is trained on a 100% synthetic dataset coming from our own automated generator. The positional tracker relies on a range of 1 to n video cameras placed around an arena of choice. The system returns the positions of the tracked objects relative to the broader world by understanding the overlapping matrices formed by the cameras and therefore these can be extrapolated into real world coordinates. In most cases, we achieve a higher update rate and positioning precision than any of the existing GPS-based systems, in particular for indoor objects or those occluded from clear sky.