Mobile Crowd Sensing and Computing (MCSC) is a replacement of static sensing infrastructure by user's mobile sensor-enhanced devices. MCSC collects user's local knowledge such as local information, ambient, and traffic conditions using sensor-enabled devices. The collected information is further aggregated and transferred to the cloud for detailed analysis. In this paper, we propose a Smartphone-based non-participatory crowd monitoring system, named CrowdTrack, to monitor the movement patterns of one or more persons (non-participatory) using unmodified Smartphones in a densely crowded environment. CrowdTrack uses the Smartphone as a sensing unit without any hardware modification to extract the MAC ids from the wireless probe requests emitted from the users' devices. MAC ids are stored and processed locally for short-term analysis and then the filtered data is uploaded to the server for better analysis and visualization. We have also developed a real-time testbed to identify mobility patterns in the data collected from our Institute campus and it is deployed to find the visiting sequences of students. Real-time experiments on a proof-of-concept prototype testbed with our dataset show the usability of our proposed system.