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Visual tracking of multiple objects is a challenging task that has gained considerable attention recently. The process involves several steps: detection, data association, and re-identification, each with its own set of challenges. Trending methods usually combine some of these steps and report impressive results in tracking benchmarks while running at practical speeds. In this work, we develop an online, real-time multi-object tracking approach that is also able to perform consumer tracking in indoor retail stores, without further tuning its design philosophy. Our model can utilize multi-modal inputs, such as RGB and thermal images when available, to improve its performance while maintaining acceptable speeds at 20FPS. Our key contribution to motion forecasting replaces the standard Kalman filters with an LSTM network to properly model long-term dependencies and target tracking. Numerical experiments of our …