Local kernel color histograms for background subtraction
نویسندگان
چکیده
In addition to being invariant to image rotation and translation, histograms have the advantage of being easy to compute. These advantages make histograms very popular in computer vision. However, without data quantization to reduce size, histograms are generally not suitable for realtime applications. Moreover, they are sensitive to quantization errors and lack any spatial information. This paper presents a way to keep the advantages of histograms avoiding their inherent drawbacks using local kernel histograms. This approach is tested for background subtraction using indoor and outdoor sequences.
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