Real Time Occluded Vehicle Detection System

Abstract

The present invention provides a real-time occluded vehicle detection system. A major challenge in computer vision is detecting and tracking vehicles in real time. However, existing algorithms fail to detect vehicles at high speed and accuracy. Therefore, an algorithm that detects vehicles with higher accuracy is required for surveillance in traffic scenarios. This paper proposed an improved algorithm for vehicle detection based on YOLO (You Only Look Once) Version 4 through Convolution Neural Network (CNN) and Hard Negative Example Mining (HNEM) dataset in the training process to improve the accuracy of the vehicle detection. In the end, videos are used to detect vehicles using a deep learning technique called You Only Look Once (YOLO).

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