Experiments with patch-based object classification
We present and experiment with a patch-based algorithm for the purpose of object classification in video surveillance. A feature vector is calculated based on template matching of a large set of image patches, within detected regions-of-interest (ROIs, also called blobs), of moving objects. Instead of matching direct image pixels, we use Gabor-filtered versions of the input image at several scales. We present results for a new typical video surveillance dataset containing over 9,000 object images. Additionally, we show results for the PETS 2001 dataset and another dataset from literature. Because our algorithm is not invariant to the object orientation, the set was split into four subsets with different orientation. We show the improvements, resulting from taking the object orientation into account. Using 50 training samples or higher, our resulting detection rate is on the average above 95%, which improves with the orientation consideration to 98%. Because of the inherent scalability of the algorithm, an embedded system implementation is well within reach.