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I would like to locate a car (front center point x,y) using a high resolution single camera. The camera setup is fixed at 1-2m high, and tilted around 25 degrees. The camera can provide images in where the front side of the car is visible. The intrinsic and extrinsic parameters are known.

So far, I tried to detect the headlights and number plates. Issues... Headlights are not detected as blobs all the time. The shape of the headlights are changing depending on the distance. Also, the number plate is not visible in the dark.

Is there a robust algorithm to detect a car? or to detect headlights? or detect number plate?How could I proceed?

Thanks in advance,

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这不是一个容易的问题，因为尺度和观点的变化。理想情况下，您需要一组从不同角度看到的汽车的训练图像，以便稍后将其中一些与您的输入图像进行匹配。然后，您需要局部特征（SIFT、SURF）或一些分类器来决定匹配。

另一方面，如果您一直在跟踪同一辆车，请查看MeanShift 算法。问题是您需要一个初始位置来进行跟踪。

于 2013-06-18T08:42:37.283 回答
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您是否每次都检测到同一辆车？如果是，那么大概外观保持一致。与其检测和识别斑点和形状，不如将比例和旋转不变特征与机器学习算法结合使用。查看SIFT和SURF特征描述符。为了方便实验，请使用 OpenCV 的特征描述和匹配实现。看看这个例子。

于 2013-06-18T08:36:11.947 回答