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A team at the Gwangju Institute of Science and Technology (GIST) in Korea, led by Moongu Jeon, implemented a technique called deep temporal appearance matching association, or Deep-TAM, to overcome short-term occlusion, which affects the ability of computer vision systems to simultaneously track objects. The framework was shown to achieve high performance without sacrificing computational speed.
Algorithms that can simultaneously track multiple objects are essential to applications that range from autonomous driving to advanced public surveillance. However, it is difficult for computers to discriminate between detected objects based on their appearance.
One example of a function that remains difficult for computers is object…READ MORE