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Traffic flow forecasting is an essential aspect of intelligent traffic management. It enables timely and proactive management of modern transport systems, increasing efficiency and resilience. However, accurately predicting short-term traffic flow is challenging due to its uncertain and interconnected nature. Traditional methods like loop detectors and high-resolution cameras have limited scalability. To address this, we propose a two-stage approach using low-resolution surveillance cameras. The first stage involves a vision-based data extraction module with calibration, vehicle detection, and tracking. Integration of Region of Interest, fine-tuning, and post-processing improves the robustness of low-resolution videos. In the second stage, a novel deep learning model extracts spatio-temporal features from historical traffic data for short-term flow prediction. The proposed model outperforms the STGCN model, achieving an 11.19% increase in MAE, a 12.37% improvement in RMSE and a 4.97% reduction in inference time. These advances highlight its potential for further research and applications in the field. [ABSTRACT FROM AUTHOR]