Computer Vision Systems for “Context-Aware” Active Vehicle Safety and Driver Assistance - Digital Signal Processing for In-Vehicle Systems and Safety - page 207

15.2.4 Road Object Detection and Tracking
Video streams, whether processed online or off-line, contain rich information
content regarding road scene. It is possible to detect and track vehicle, lane
markings, and pedestrians and recognize road signs using a frontal camera and
some additional sensors such as radar.
It is of crucial importance to be able to detect, recognize, and track road objects
for effective collision avoidance or driver assistance system. In this chapter, we
present our current progress in lane tracking and road sign recognition also reported
in [22], adding a system utility analysis here.
15.2.5 Lane Detection and Tracking
There has been extensive work in developing lane tracking systems in the area of
computer vision. These systems can be potentially utilized in driver assistance
systems related to lane keeping and lane change. In [23], a comprehensive compar-
ison of various lane-position detection and tracking techniques is presented. From
that comparison, it is clearly seen that most lane tracking algorithms do not perform
adequately so as to be employed in actual safety-related systems; however, there are
encouraging advancements towards obtaining a robust lane tracker. A generic lane
tracking algorithm has the following modules: a road model, feature extraction,
post-processing (verification), and tracking. The road model can be implicitly
incorporated as in [24] using features such as starting position, direction, and
gray-level intensity. Model-based approaches are found to be more robust com-
pared to feature-based methods. For example, in [25], a B-snake is used to represent
the road. Tracking lanes in real traffic environment is an extremely difficult
problem due to moving vehicles, unclear/degraded lane markings, and variation
of lane marks, illumination changes, and weather conditions. In [26], a probabilistic
framework with particle filtering was suggested to track the lane candidates
selected from a group of lane hypotheses. A color-based scheme is used in [27];
shape and motion cues are employed to deal with moving vehicles in the traffic
scene as well.
15.2.6 Road Sign Recognition
Methods used for automatic road sign recognition can be classified into three groups:
color based, shape based, and others. The challenges in recognition of road signs