Abstract:
Object tracking and detection has become a large field with a wide range of algorithms
being used as a result in several computer vision applications. Because of the number
of cameras used to cover a large area, these applications are constrained by the cost
of each node, the power consumption, the robustness of the tracking, the processing
time, and the ease of deployment of the system. To meet these challenges, the use of
low-power and low-cost embedded vision platforms to achieve reliable tracking becomes
essential in the field of cameras.
In this project, we propose a financially cheap and fast object detection and tracking
model. The tracking is implemented in C++ with OpenCV on a Raspberry Pi 4 and
the Raspberry Pi Camera Module as camera feed. To be able to detect and follow an
object a pan and tilt system is built to be able to follow the object using tilting and
panning motion. Two different detection algorithms have been used, object detection
the default one that detects the whole object, and the second one when the object
starting to get closer to the camera the system starts face detection.
The system created is able to detect and track a moving object and keeping it in the
center of the image
