| Summary: | Real-time tracking and object recognition is a large field with many possible applications. In this thesis we present a technical demo of a stereoscopic tracking system using artificial neural networks (ANN) and also an overview of the entire system, and its core functions. We have implemented a system able of tracking an object in real time at 60 frames per second. Using stereo matching we can extract the object coordinates in each camera, and calculate a distance estimate from the cameras to the object. The system is developed around the Xilinx ZC-706 evaluation board featuring a Zynq XC7Z045 SoC. Performance critical functions are implemented in the FPGA fabric. A dual-core ARM processor, integrated on the chip, is used for support and communication with an external PC. The system runs at moderate clock speeds to decrease power consumption and provide headroom for higher resolutions. A toolbox has been developed for prototyping and the aim has been to run the system with a one-push-button approach. The system can be taught to track any kind of object using an eight bit 32 × 16 pixel pattern generated by the user. The system is controlled over Ethernet from a regular workstation PC, which enables it to be very user-friendly.
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