GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
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Citations
Lane tracking with omnidirectional cameras: algorithms and evaluation
Lane marking detection by side Fisheye Camera
A real-time fuzzy hardware structure for disparity map computation
Vision-based vehicle body slip angle estimation with multi-rate Kalman filter considering time delay
A laser and a camera for mobile robot navigation
References
Image Analysis and Mathematical Morphology
Low-power CMOS digital design
Image Analysis Using Mathematical Morphology
Low-Power CMOS Digital Design
Principles of Interactive Computer Graphics
Related Papers (5)
Frequently Asked Questions (14)
Q2. How long does it take to generate a remapped image?
The remapping process takes three 50 ns clock cycles per pixel, giving a total of about 3 ms togenerate a 128 128 remapped image.
Q3. What is the purpose of the removal of the perspective effect?
The removal of the perspective effect allows to detect road markings through an extremely simple and fast morphological processing that can be efficiently implemented on massively parallel SIMD architectures.
Q4. How can the GOLD system work in pipelined?
Since the GOLD system is composed of two independent computational engines (the PAPRICA system, running the low-level processing, and its host computer, running the medium-level processing), it can work in pipelined.
Q5. What is the choice of for the remapping phase?
The choice of depends on the road markings width, on the image acquisition process, and on the parameters used in the remapping phase.
Q6. What is the last phase of the whole computational cycle?
The last phase of the whole computational cycle is the displaying of results on the control panel, issuing warnings to the driver.
Q7. What is the main problem in the detection of road boundaries?
The main problems that must be faced in the detection of road boundaries or lane markings are: 1) the presence of shadows, producing artifacts onto the road surface, and thus altering its texture, and 2) the presence of other vehicles on the path, partly occluding the visibility of the road.
Q8. What is the simplest way to determine the maximum value of the histogram?
In order to allow a nonfixed road geometry (and also the handling of curves) the histogram is lowpass filtered; finally, its maximum value is determined.
Q9. Why is the polar histogram used for the detection of triangles so small?
Due to the small distance between and instead of computing two different polar histograms (having focus on and , a single one is considered.
Q10. What is the way to verify the shape of the horopter?
the horopter cannot be overlapped with the plane (representing the flat road model) using only camera vergence; for this purpose, electronic vergence, such as inverse perspective mapping (IPM), is required.
Q11. How can the power consumption of dynamic systems be considered proportional to where represents the capacitance?
The power consumption of dynamic systems can be considered proportional to where represents the capacitance of the circuit, is the clock frequency, and is the voltage swing.
Q12. How many pixels is the vertical shift between two subsequent remapped images?
Considering an operational vehicle speed of 100 km/h and the MOB-LAB calibration setup, the vertical shift between two subsequent remapped images corresponding to two frames acquired with a temporal shift of 100 ms is only 7 pixels.
Q13. What is the main problem in the detection of obstacles?
The farther the obstacle, the smaller the portion of triangles detectable in the difference image, and thus the lower the amplitude of peaks in the polar histogram; nevertheless, for sufficiently high obstacles (e.g., vehicles at about 50 m far from the cameras), the main problem is not the detection of peaks, but their joining, as shown in Fig. 28(a)–(c).
Q14. How many time slots does the GOLD system require?
As shown in Fig. 24, the whole processing (lane and obstacles detection) requires five time slots (100 ms);2 the GOLD system works at a rate of 10 Hz.