Phase2

This release consists on updated UML diagrams and revised implementation of previously designed stubs.

The project detects human obstacles and tracks them while outputting the location information (x, y, z) in inches, preferably in autonomous vehicle systems. The system work such that the distance from any camera reference frame is converted to the robot reference frame and made know to the robot for perception tasks.

The API consists of a detection and tracking sub-parts,
In detection, a video stream is initialized and human obstacles are detected using face recognition in OpenCV by employing a ResNet detection.
In tracking, the detected obstacles are assigned unique IDs for tracking and their location (x, y, z) in robot reference frame is calculated.

Phase1

This release consists of the UML design, skeleton and stub design for the classes and unit tests for the methods in classes.
There are three library skeletons built,
A detection library, to initialise the video stream and detect face for human obstacle detection.
A tracking library, to assign IDs, track the motion of the obstacle and output obstacle location based on robot reference frame.
A display library, used in testing the API implementation.