Accuracy Evaluation of Obstacle Distance

For evaluation of distance measurement, we compare length of real line with the length which estimated by the proposed procedure to obtain the distance measurement error. By testing 590 data such as illustrated in Fig. 5-10 the land marking, the error of distance measurement within short and long range respectively is shown in Table 5-4.

Due to the geometric characteristic of calibration procedure, the calibration error in the far range will be enlarged. Therefore, the average distance measure error of near range is about 0.16m but of far range is about 0.68m. The experimental results demonstrate that estimation for distance of target position is accurately.

Fig. 5-10 Land marking for distance measurement

Near range (3~5m) Far range (5~8m)

Average Distance Error (m) 0.16 m 0.68 m

Table 5-4 Experimental result of distance measurement

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Chapter 6

Conclusions and Future Work

For generic obstacle detection, researchers have proposed many methods which focus on stereo vision. Compared to other researches we proposed a system which could automatic detect obstacle with only a single camera mounted on a moving vehicle. Besides, the movement of ego-vehicle which is generally acquired by external sensors such as odometer, we propose a ground movement estimation method that can only adopt image knowledge to obtain these information effectively.

In our research, we intend as obstacle any object that can obstruct the vehicle’s driving path or anything raise out significantly from the road surface. Therefore, we propose a ground movement compensation based approach to detect non-planar objects. In addition, adopting different characteristics between planar and non-planar object result from IPM to detect obstacle. The proposed ground movement estimation technique is employing road detection to assist in obtaining most useful ground features in the image, and analysis the principal distribution of optical flow of these feature points, the ground movement for compensation is obtained accurately. The accurately ground movement information can improve the performance of obstacle detection. Thus, the experimental results on many conditions which could occur during backing up period are already used to demonstrate the effectiveness of the proposed obstacle detection algorithm. Finally, we utilize calibration procedure to achieve distance measurement for every detected obstacle. By indicating information of obstacle distance, driver can realize objects are near or far away from our ego-vehicle. Besides, the estimation for distance of target position is verified by

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practical measurement, our proposed method can achieve the accurately.

So far, the proposed obstacle detection algorithm can operate well in variant conditions during the backing up maneuver. However, a weak point of the proposed compensation based detection is the detection when vehicle is stationary. In the future, the work should be committed toward utilizing single frame to detect non-planar objects to improve the performance on a stationary scene.

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