General Road Detection Algorithm, a Computational Improvement

International audienceThis article proposes a method improving Kong et al. algorithm called Locally Adaptive Soft-Voting (LASV) algorithm described in " General road detection from a single image ". This algorithm aims to detect and segment road in structured and unstructured environments. Evaluation of our method over different images datasets shows that it is speeded up by up to 32 times and precision is improved by up to 28% compared to the original method. This enables our method to come closer the real time requirements

Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features

International audienceThis paper deals with real-time visual detection, by mono-camera, of objects categories such as cars and pedestrians. We report on improvements that can be obtained for this task, in complex applications such as advanced driving assistance systems, by using new visual features as adaBoost weak classifiers. These new features, the “connected controlpoints” have recently been shown to give very good results on real-time visual rear car detection. We here report on results obtained by applying these new features to a public lateral car images dataset, and a public pedestrian images database. We show that our new features consistently outperform previously published results on these databases, while still operating fast enough for real-time pedestrians and vehicles detection

Introducing New AdaBoost Features for Real-Time Vehicle Detection

International audienceThis paper shows how to improve the real-time object detection in complex robotics applications, by exploring new visual features as AdaBoost weak classifiers. These new features are symmetric Haar filters (enforcing global horizontal and vertical symmetry) and N-connexity control points. Experimental evaluation on a car database show that the latter appear to provide the best results for the vehicle-detection problem

Spatial-UNet: Deep Learning-Based Lane Detection Using Fisheye Cameras for Autonomous Driving

International audienc