Automatic Speed Measurement and License Plate Reader for Motor Vehicles using CNN

Assessing traffic surveillance data is a prevalent utilization of deep learning methodologies. Video processing techniques were employed to develop the system aimed at determining the velocity of vehicles. The analysis of video speed is conducted without the requirement of camera calibration. Frame subtraction and masking techniques are employed to effectively segregate moving vehicles. The suggested system comprises four primary components. Object detection techniques are employed in the initial phase to identify and locate moving vehicles within a given footage. Real-time vehicle recognition leverages the utilization of R-CNN and deep learning techniques. In the subsequent section, algorithms are employed to observe the movement of objects within individual video frames. Optical flow methods are employed in the third segment to ascertain the velocities of the respective vehicles. In the preceding part, the proposed work have utilized image processing techniques in conjunction with deep learning methodologies to accurately decipher the license plates of the automobiles under our surveillance. The experimental evaluation involving the utilization of traffic film demonstrated a system performance of 97% in precisely quantifying speed while achieving a license plate identification accuracy of 95%. The incorporation of state-of-the-art traffic management systems has facilitated the implementation of real-time speed monitoring and license plate reading capabilities. This facilitates the deterrence of automobile theft and the implementation of traffic regulations. Additionally, the methodology has the capability to identify traffic patterns and pinpoint regions of congestion, thus assisting in the effective management of traffic.