FAST ACCURATE PEDESTRIAN DETECTION USING A MPL-BOOSTED CASCADE OF WEAK FIK-SVM CLASSIFIERS
Junqiang Wang, Huadong Ma, Anlong MingAbstract
We address the problem of pedestrian detection in still images. Current pedestrian detection systems are hard to improve both speed and accuracy simultaneously. In order to achieve a balance between speed and accuracy, we propose a novel MPL-Boosted cascade of weak FIK-SVM classifiers. Our method achieves high recall while taking the speed-advantage of cascade-of-rejectors approach. Each feature in our algorithm corresponds to a 66-D HOG-LBP feature vector that describe a block. The weak classifiers we use are the separating hyper-plane computed by using a FIK-SVM. We use MPL-Boost to select features from a large set of possible blocks. The integral image and convoluted trilinear interpolation are used for rapid calculation of block feature. For a 320*240 image, the system can process 16 frames per second with sparse scan, while defeat the accuracy level of existing methods.
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