7,279 research outputs found
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector
Multiple object tracking (MOT) in urban traffic aims to produce the
trajectories of the different road users that move across the field of view
with different directions and speeds and that can have varying appearances and
sizes. Occlusions and interactions among the different objects are expected and
common due to the nature of urban road traffic. In this work, a tracking
framework employing classification label information from a deep learning
detection approach is used for associating the different objects, in addition
to object position and appearances. We want to investigate the performance of a
modern multiclass object detector for the MOT task in traffic scenes. Results
show that the object labels improve tracking performance, but that the output
of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
Real-time automated road, lane and car detection for autonomous driving
In this paper, we discuss a vision based system for autonomous guidance of vehicles. An autonomous intelligent vehicle has to perform a number of
functionalities. Segmentation of the road, determining the boundaries to drive in and recognizing the vehicles and obstacles around are the main tasks for vision guided vehicle navigation. In this article we propose a set of algorithms which lead to the solution of road and vehicle segmentation using data from a color camera. The algorithms described here combine gray value difference
and texture analysis techniques to segment the road from the image, several geometric transformations and contour processing algorithms are used to segment lanes, and moving cars are extracted with the help of background modeling and estimation. The techniques developed have been tested in real road images and the results are presented
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