Lane Discovery in Traffic Video

Video sensing has become very important in Intelligent Transportation Systems (ITS) due to its relative low cost and non-invasive deployment. An effective ITS requires detailed traffic information, including vehicle volume counts for each lane in surveillance video of a highway or an intersection. The multiple-target, vehicle-tracking and counting problem is most reliably solved in a reduced space defined by the constraints of the vehicles driving within lanes. This requires lanes to be pre-specified. An off-line pre-processing method is presented which automatically discovers traffic lanes from vehicle motion in uncalibrated video from a stationary camera. A moving vehicle density map is constructed, then multiple lane curves are fitted. Traffic lanes are found without relying on possibly noisy tracked vehicle trajectories

Geometric models for video surveillance in road environments: vehicle tailgating detection

Traffic accidents constitute one of the main causes of death in many countries. Despite the current efforts devoted to mitigate the effects of road incidents, there are still some variables affecting this problem which are not yet under control or regulation. Spain, for instance, still lacks official regulations about especially risky driving behaviours, such as tailgating. In many cases, the rationale behind is that these behaviours are hard or expensive to detect reliably, thus limiting the extent of the automatic detection systems. This paper proposes a method to identify certain elements in road scenarios, define geometric models that allow computing quantitative measures of the scene and, consequently, detect offending driving behaviours. In this work, we have focused on the particular case of study of tailgating detection. However, the proposed geometric models might become the basis of many other useful applications.Ingeniería de Sistemas Audiovisuale

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

Detection of vehicle occlusion using a generalized deformable model

This paper presents a vehicle occlusion detection algorithm based on a generalized deformable model. A 3D solid cuboid model with up to six vertices is employed to fit any vehicle images, by varying the vertices for a best fit. The advantage of using such a model is that the number of parameterized vertices is small which can be easily deformed. Occlusion is detected by recording the changes in the Area Ratio and the dimensions of the generalized deformable model. Our tests show that the new modeling algorithm is effective in detecting vehicle occlusion.published_or_final_versio

CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos

Road traffic scene reconstruction from videos has been desirable by road safety regulators, city planners, researchers, and autonomous driving technology developers. However, it is expensive and unnecessary to cover every mile of the road with cameras mounted on the road infrastructure. This paper presents a method that can process aerial videos to vehicle trajectory data so that a traffic scene can be automatically reconstructed and accurately re-simulated using computers. On average, the vehicle localization error is about 0.1 m to 0.3 m using a consumer-grade drone flying at 120 meters. This project also compiles a dataset of 50 reconstructed road traffic scenes from about 100 hours of aerial videos to enable various downstream traffic analysis applications and facilitate further road traffic related research. The dataset is available at https://github.com/duolu/CAROM.Comment: Accepted to IEEE ICRA 202

Vehicle shape approximation from motion for visual traffic surveillance

In this paper, a vehicle shape approximation method based on the vehicle motion in a typical traffic image sequence is proposed. In the proposed method, instead of using the 2D image data directly, the intrinsic 3D data is estimated in a monocular image sequence. Given the binary vehicle mask and the camera parameters, the vehicle shape is estimated by the four stages shape approximation method. These stages include feature point extraction, feature point motion estimation between two consecutive frames, feature point height estimation from motion vector, and the 3D shape estimation based on the feature point height. We have tested our method using real world traffic image sequence and the vehicle height profile and dimensions are estimated to be reasonably close to the actual dimensions.published_or_final_versio