In this diploma thesis, a state-of-the-art visual tracker is implemented and evaluated on the embedded device Luxonis OAK-1. The tracker STARK is chosen, which belongs to the family of deep neural network-based trackers. More specifically the tracker uses the novel transformer neural network architecture, which is making its way into increasingly more best performing visual trackers. During the implementation process we had to modify the tracker, convert it to the OpenVINO format, which can be used for inference on the embedded device. To run the processing completely on the embedded device the correct pipeline architecture was also developed, which allows for all the processing to be executed on the embedded device and consequently allows autonomous operation of the embedded device. With this level of autonomy, we successfully decoupled tracking performance from the performance of the host system. We evaluated all versions of the tracker on the VOT2021 and VOT2022. With the evaluation we proved that the tracker ported to the embedded device doesn't lose any tracking performance.
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