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The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project&#8217;s aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens&#8217; privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable <i>Agnosticity</i> framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.