Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

Assembling convolution neural networks for automatic viewing transformation

Images taken under different camera poses are rotated or distorted, which leads to poor perception experiences. This paper proposes a new framework to automatically transform the images to the conformable view setting by assembling different convolution neural networks. Specifically, a referential 3D ground plane is firstly derived from the RGB image and a novel projection mapping algorithm is developed to achieve automatic viewing transformation. Extensive experimental results demonstrate that the proposed method outperforms the state-ofthe-art vanishing points based methods by a large margin in terms of accuracy and robustness

Mobile mapping for the automated analysis of road signage and delineation

A portable mobile stereo vision system designed for the assessment of road signage and delineation (lines and road studs or 'cat eyes') in low light conditions is presented. This novel system allows both geometric and photometric measurements to be made on objects in a scene. Using the system, it has been shown that retro-reflectors, and in particular road signs, can be identified by nature of their reflective properties. In addition, a novel imaging application has been investigated that facilitates the detection of defective road studs. Any objects examined can also be positioned on a national grid through the fusion of stereo vision with global positioning system technology. Automated feature extraction and analysis routines make the system fully autonomous

Real-Time Mapping Using Stereoscopic Vision Optimization

This research focuses on efficient methods of generating 2D maps from stereo vision in real-time. Instead of attempting to locate edges between objects, we make the assumption that the representative surfaces of objects in a view provide enough information to generate a map while taking less time to locate during processing. Since all real-time vision processing endeavors are extremely computationally intensive, numerous optimization techniques are applied to allow for a real-time application: horizontal spike smoothing for post-disparity noise, masks to focus on close-proximity objects, melding for object synthesis, and rectangular fitting for object extraction under a planar assumption. Additionally, traditional image transformation mechanisms such as rotation, translation, and scaling are integrated. Results from our research are an encouraging 10Hz with no vision post processing and accuracy up to 11 feet. Finally, vision mapping results are compared to simultaneously collected sonar data in three unique experimental settings