SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring

In this paper, we develop SafeStep, an interactive browser-based semantic communication platform for live pedestrian safety monitoring. SafeStep extracts pedestrian information from four live traffic-camera feeds, transmits it through a semantic communication transceiver over a software-emulated Additive White Gaussian Noise (AWGN) channel, and renders user-specific positions, trajectories, and risk labels. The platform allows each user to select the transceiver, Signal-to-Noise Ratio (SNR), codelength, and Age of Information (AoI) and view the resulting pedestrian reconstruction. SafeStep compares a recently proposed semantic communication design called Meta-VIB with five baseline transceivers. Meta-VIB uses a compact neural model with only 4.16 million parameters to generalize across varying SNR, codelength, and AoI values without online retraining. Meta-VIB achieves mean task-loss reductions of up to 92.1%. On one high-end GPU server, the integrated concurrent-access workload maintains the target 5 frames/s through 20 users. At 100 users, each requesting a distinct configuration, SafeStep records no request failures and a mean application response time below 1 s, but its mean per-browser frame rate falls to approximately 1 frame/s. To our knowledge, SafeStep is the first real-time semantic communication platform to make AoI-induced downstream degradation directly observable in live monitoring applications.