V-DaT: A Robust method for Vehicle Detection and Tracking

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions reducing false detection. In this work, a method detection tracking is proposed. The proposed model considers background subtraction concept moving but unlike conventional approaches, here numerous algorithmic optimization approaches have applied multi-directional filtering fusion based subtraction, thresholding, directional morphological operations addition, blob analysis adaptive bounding box used Detection Tracking. Performance Proposed work measured on Standard Dataset results are encouraging.