Quantum-enhanced Vehicle Detection and Speed Monitoring using OpenCV

Quantum based vehicle detection is the innovative integration of Quantum Machine Learning (QML) techniques with classical computer vision methods to enhance vehicle detection and speed tracking systems using OpenCV. Traditional approaches in vehicle detection and speed tracking are often hindered by computational inefficiencies and accuracy limitations in complex traffic scenarios. Our proposed hybrid quantum-classical framework aims to overcome these complications by exploiting the computational advantages of quantum computing. In this study, we employ Quantum Principal Component Analysis (QPCA) for feature extraction and data dimensionality reduction, optimizing the input data for improved processing efficiency. The core detection mechanism integrates Quantum Neural Networks (QNNs) with classical Convolutional Neural Networks (CNNs), enhancing the model’s accuracy in identifying vehicles within video frames. For speed tracking, we apply quantum optimization techniques to refine the tracking of vehicle positions across consecutive frames, ensuring precise speed calculation. Using OpenCV for real-time video processing and Qiskit for quantum computations, the findings from the experiments indicate significant improvements in both detection a precision, accuracy and computational efficiency. The hybrid quantum-classical model showcases the potential of quantum computing in advancing computer vision applications, particularly in intelligent transportation systems. This research highlights the transformative impact of QML techniques on practical applications and paves the way for further advancements in quantum-enhanced vehicle detection and speed tracking.