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  1. PO
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    <p>You did the following steps:</p> <ol> <li>Match key points in 2 images.</li> <li>Assuming the match was correct, calculate homography (projection matrix).</li> <li>Use the homography to project the corners of original image to draw a quadrilateral shape (which you refer to as rectangle under) perspective transformation.</li> </ol> <p>The problem you are having is that when step 1 fails you get a wrong homography in step 2 (wrong matrix) and when you project the corners in step 3, they can fall out of the image and you don't see the lines.</p> <p>What you actually want is a way to know whether the homography you calculated is of a correct form. To do this please see the answer here: <a href="https://stackoverflow.com/questions/14954220/how-to-check-if-obtained-homography-matrix-is-good/14965352#14965352">How to check if obtained homography matrix is good?</a> Use it to test if your homography is correct. If not, you know that the match resulted in a failure. If it is correct, you can draw a rectangle and you will see it but it might be not so accurate if the match between key points was not accurate.</p> <p>Finally, I think that your algorithmic approach is wrong. Recognizing/detecting a vehicle from top view by matching it to image of vehicle from frontal view is a dead end. You should not use key points matching at all. Just mark manually all the vehicles on the images and send it to SVM. If this is too much work use Mechanical Turk platform to label the vehicles automatically. In conclusion - key points match is an approach that just does not suits your needs because it has a strong assumption that the appearance of the car in both images is similar. In your case those images are too different (due to the 3D structure of the car and different viewing angles)</p>
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    1. POObject Detection using Surf
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    1. USDanielHsH
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    1. CO+1 , Should i use SURF SVM ?
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    2. COI am not sure that surf are the right features. Why do you think that SURF features will capture the difference between cars? SURF catches very small local variations (like cellphone on the dashboard, or the person in the drivers seat) while cars marks are different in big scale (shape/size). I would use large features features like ratio of car width to length, color, shape of the contour of the car. Image patches from the front and the rear of the car, patch in the front of the car which captures the symbol, etc... If you use local features, calculate histograms and use them as descriptors
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    3. COyeah okay thanks
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