Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.