Fast accurate pedestrian detection using a MPL-Boosted cascade of weak FIK-SVM classifiers

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.