| Resumo: | Multiple object detection and tracking is one of the main topics in computer vision. The task is to identify all the objects of interest in a frame of a video and to assign an unique ID to those instances that correspond to the same object while it appears in the scene. This is a fundamental task of many video analytics applications like traffic monitoring or video surveillance, which usually requires real-time processing speed and its execution on different hardware devices. In this PhD Thesis we address the topic of Real-Time Multiple Object Detection and Tracking Systems, combining state-of-the-art detectors, trackers and data association techniques. Particularly, we focus on the design of Real- Time Multiple Object Detection and Tracking systems for both server architectures and embedded devices, that are able to work with dozens of objects in real-time.
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