Hybrid Comparative Solution Boosts Multi-Object Tracking
GWANGJU, South Korea, Aug. 4, 2021 — 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