Vehicle Detection and Localization for Autonomous Traffic Monitoring Systems in Unstructured Crowded Scenes

Image/video processing has been one of the major developments in the recent history with its applications in areas of Road safety, military, medical and agriculture fields. Due to its complexity a generic solution for multiple object detection in extremely crowded scenes remains to be found. Traditional methods of optical flow, connected component analysis and image segmentation have been extensively studied in image processing and video processing material. With recent developments of machine learning and numerical optimization techniques the use of deep neural networks are getting frequent in image processing applications. Among such deep learningbased methods commonly used in this context are RCNN variants, Mask RCNN and YOLOv3. An exhaustive comparison of the traditional methods and deep learning-based methods and also deep learning methods are discussed in this paper. This study will be of use in selection of a method for any extremely crowded scene object detection problem.