Department of Informatics
نویسندگان
چکیده
Accurate pedestrian detection is important for many future technologies. Detection based on vision data is widely used in many applications today, however it seems like the maximum accuracy that can be achieved has almost been reached. Therefore, other ways of improving detection such as using depth data have to be considered. In this thesis I will describe the Integral Channel Features detector, a detection algorithm based on visual data. I will also describe how the algorithm can be effectively trained using Adaptive Boosting techniques. Finally, I will give an overview of how depth data can be used to improve the performance of the detector.
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