Abstract
Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.
Highlights
Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle
There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment
Cameras are cheaper, smaller, and of higher quality than ever before. Parallelization, such as multi core processing and graphical processing units (GPUs), of computing platforms geared in recent years
Summary
Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. Cameras are cheaper, smaller, and of higher quality than ever before Parallelization, such as multi core processing and graphical processing units (GPUs), of computing platforms geared in recent years. Such hardware advances allow real-time implementation for vehicle detection using computer vision approaches. Detect and track on-road vehicles in real time become commonplace for research studies to report the ability to reliably over extended periods [2]. The aggregate of this spatiotemporal information from vehicle detection and tracking can be used to identify maneuvers and to learn, model, and classify onroad behavior. Various works of visionbased vehicle detection techniques has been reviewed
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