7. preceding vehicle detection - yonsei university · 2014-12-29 · • u-shape (i.e., one...

E-mail: [email protected]://web.yonsei.ac.kr/hgjung

7. Preceding Vehicle 7. Preceding Vehicle DetectionDetection


Preceding Vehicle Preceding Vehicle 정보가정보가 필요한필요한 시스템은시스템은??

• ACC (Adaptive Cruise Control)

• CW (Collision Warning)

• PCS (Pre-Crash Safety) system

• CA (Collision Avoidance)

• Stop&Go

• Autonomous Following

• PCDN (Pre-Crash Dipping Nose)


ACCACC

• Mercedes-Benz, Distronic Plus• Precrash warning + Full-range ACC

http://www.youtube.com/watch?v=T8NyctsNuIU


PCS (PrePCS (Pre--Crash Safety) SystemCrash Safety) System

• Radar• Audible warning + Seat belt tensioner

http://www.youtube.com/watch?v=tjYAng48o2s



• Radar

• Vehicle Motion Sensors

ABS BAS ESP



http://www.youtube.com/watch?v=6nNf1ceiJZc

ESP



http://www.youtube.com/watch?v=fMqEf4NwsKg


Collision Warning and Avoidance SystemCollision Warning and Avoidance System

• Audible and visible warning• Full automatic braking• BSD + Collision Warning + Collision Avoidance

http://www.youtube.com/watch?v=SQh5Fp2wJyE


Side Impact ReductionSide Impact Reduction

• Radar• Active Suspension Actuator

Vehicle height adjustment prevents the struck vehicle from being impacted at its weakest side structure, i.e., the door.By using radar and controlling the suspension systems of high-profile vehicles, the front end of SUV's can be lowered to the level of a passenger car in the event of a crash, giving the occupants in the passenger car a four-times better chance of surviving. The SUV then automatically resumes its original height in less than 20 seconds.


VisionVision--Based Preceding Based Preceding Vehicle DetectionVehicle Detection


Two Steps of Vehicle Detection [2][3]Two Steps of Vehicle Detection [2][3]


Hypothesis Generation (HG) Methods [2][3]Hypothesis Generation (HG) Methods [2][3]


Hypothesis Verification (HV) Methods [2][3]Hypothesis Verification (HV) Methods [2][3]


HG: KnowledgeHG: Knowledge--Based Methods [2][3]Based Methods [2][3]

• Images of vehicles observed from

rear or frontal views are in general symmetrical in the horizontal and vertical directions.

• Sensitive to noise in the homogeneous areas.

Symmetry



• By investigating image intensity, it was found that the area underneath a

vehicle is distinctly darker than any other areas on an asphalt paved road.• To segment the shadow area, a low and a high threshold are required.

However, it is obvious that it is hard to find a low threshold for a shadow area. The high threshold can be estimated by analyzing the gray level of the “free driving space”—the road right in front of the prototype vehicle.

Shadow



• Exploiting the fact that vehicles in general have a rectangular shape with

four corners (upper-left, upper-right, lower-left, and lower-right).• Four templates, each of them corresponding to one of the four corners,

were used to detect all the corners in an image, followed by a search method to find the matching corners (i.e., a valid upper-left corner should have a matched lower-right corner).

Corner



• Different views of a vehicle, especially rear/frontal views, contain many

horizontal and vertical structures, such as rear window, bumper, etc.• A multi-scale approach which combines sub-sampling with smoothing to

hypothesize possible vehicle locations more robustly was proposed.

Vertical/Horizontal Edges



• The presence of vehicles in an image causes local intensity changes. Due

to general similarities among all vehicles, the intensity changes follow a certain texture pattern.

• Entropy-based: only regions with high entropy• Co-occurrence matrices

Texture


• Symmetry, Corners and Texture are effective in relatively simple environments with no or little clutter. But employing those cues in complex environments will introduce many false positives. However we can use them as one cue (so we trust part of them).

• Inherent difficulties of color-based object detection in outdoor settings (weather/illumination);

• Under perfect weather conditions, using shadow information can be very successful. However, bad weather conditions or bad illumination conditions make road pixels quite dark, causing this method to fail.

• Utilizing horizontal and vertical edges for HG is probably the most promising among the knowledge-based approaches reported in the literature. The main problem with this approach is that it depends on a number of parameters (threshold) that could affect system performance and robustness. Also, edge-based method is not appropriate for distant vehicles.



Inverse Perspective Mapping [2][7]

Bird’s eye view generation

HG: StereoHG: Stereo--VisionVision--Based MethodsBased Methods


Inverse Perspective Mapping [2][7]



• Feature-based stereo matchingDisparity Histogram-Based [4]



• Object will make a peak in disparity histogram.

Disparity Histogram-Based [4]



V-disparity [5]

The two image planes of the stereo sensor are supposed to belong merely to the same plane and are at the same height above the road. This camera geometry means that the epipolar lines are parallel.



We suppose that a disparity map IΔ has been computed from the stereo image pair.Let H be the function of the image variable IΔ such that H(IΔ) = IvΔ. We call IvΔ as the ”v-disparity” image.

H accumulates the points with the same disparity that occur on a given image line i.Once IΔ has been computed, IvΔ is built by accumulating the pixels of same disparity in IΔ along the v axis.

i: image line, vV-disparity [5]



V-disparity [6]


The corresponding v-disparity representation is computed by accumulating

the points with the same disparity value that occur on a given image line.


Flat road geometry

The longitudinal profile of the road is therefore a straight line in IvΔ. Robust detection of this straight line can be achieved by applying a robust 2D processing to IvΔ.

The pitching and relative height of the stereo sensor are dynamically estimated by simply extracting the straight line of the road.

V-disparity [5]



It is also possible to classify all the pixels that belong to the obstacles. The process consists in comparing the disparity of all the points of the disparity map with the disparity of the road surface for a given line of analysis: the pixel belongs to an obstacle if its disparity is different from the latter.

V-disparity [5]



Eventually, bounding boxes are build around detected obstacles by clustering the pixels of the disparity map which disparity equals the one of extracted obstacle straight lines in the “v-disparity” image (see Fig. 5).

The free road surface (surface free of obstacles) is deduced from this obstacle map using a growing area algorithm.

V-disparity [5]



Stixel, 6D Vision [8]

1024×440 image

205 stixels (with 5 pixel width)

SGM (Semi-Global Matching)

Real-time dense stereo using a Xilinx FPGA platform



Stixel, 6D Vision [8]



HG: StereoHG: Stereo--VisionVision--Based Methods [2][3]Based Methods [2][3]


HG: Optical FlowHG: Optical Flow--Based [2][3]Based [2][3]


HV: TemplateHV: Template--Based [2][3]Based [2][3]

• Template-based methods use predefined patterns of the vehicle class

and perform correlation between the image and the template.

• Some of the templates reported in the literature represent the vehicle

class “loosely,” while others are more detailed.

Loose template

• the presence of license plates and rear windows

• U-shape (i.e., one horizontal edge, two vertical edges, and two corners

connecting the horizontal and vertical edges

• Moving edge closure: moving points edge detection external edge

connection

• Bounding box: symmetric, with a range of aspect ratio


HV: AppearanceHV: Appearance--Based [2][3]Based [2][3]


Discussion of HV Methods [2][3]Discussion of HV Methods [2][3]

• In general appearance-based methods are more accurate than template-based methods, however they are more costly due to classifier training.

• [1] mentioned that the best approach in terms of accuracy was found to be Gabor features with SVMs, yielding an error rate of 5.33 percent with a false positives rate of 3.46 percent and a false negatives rate of 1.88 percent.


Tentative Conclusion [2][3]Tentative Conclusion [2][3]

• Robust: choose more than one cue for HG• Inexpensive computation: multi-resolution images• Fusion: combine laser and camera together

• Practical problem: instability of camera position If we can over this

practical problem, stereo-based method is accurate.


ReferencesReferences

1. Andrew Blake and Michael Isard, “Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion,” Springer-VerlagLondon Limited, 1998.

2. Zehang Sun, George Bebis, and Ronald Miller, “On-Road Vehicle Detection: A Review,” IEEE Trans. PAMI, Vol. 28, No. 5, May 2006, pp. 1-18.

3. Chao Gao, “Vehicle Detection (Gao),” Lecture Material of Advanced Topics in Mobile Robotics: The DARPA Urban Challenge (CSE398-012/498-012), Spring 2007, LEHIGH University, available at http://www.cse.lehigh.edu/~spletzer/duc_s07/duc_s07.html

4. U. Franke, I. Kutzbach, “Fast Stereo based Object Detection for Stop&Go Traffic,” IEEE IVS, 1996, 339-344.

5. Raphael Labayrade, Didier Aubert, Jean-Philippe Tarel, “Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through “V-disparity” Representation,” IEEE IVS 2002, pp. 646-651, vol.2.

6. Angel D. Sappa, Rosa Herrero, Fadi Dornaika, David Gerónimo,and Antonio López, “Road Approximation in Euclidean and v-Disparity Space: A Comparative Study,” LNCS 4739 (EUROCAST 2007), pp. 1105-1110, 2007.

7. Bertozzi, M. Broggi, A., “GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection,” IEEE Trans. Image Processing, Vol. 7, No. 1, Jan. 1998, pp. 62-81.

8. Pfeiffer, D., Franke, U., “Modeling Dynamic 3D Environments by Means of The Stixel World,”IEEE Intelligent Transportation Systems Magazine, Vol. 3, No. 3, Fall 2011, pp. 24-36.

7. preceding vehicle detection - yonsei university · 2014-12-29 · • u-shape (i.e., one...

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