An Extended Filtered Channel Framework for Pedestrian Detection

Pedestrian detection is an important example of object detection and has attracted much attention. Many works have shown that good image features provide high detection accuracy, and a few works have investigated enhancing low-level features (e.g., gradient and color features) using a filtered layer (i.e., convolutional layer) to obtain enhanced features or filtered channel features. To investigate whether these features are saturated, this paper adopts the concept of filtered channel features and strengthens them by adding more convolutional layers. Acting as convolution kernels, multilayer filters are applied to low-level features to obtain the extended filtered channel features, providing a powerful feature extractor with multilayer transformation for pedestrian detection. The proposed extended filtered channel framework (ExtFCF) achieves competitive performance on widely used benchmark datasets (Caltech, INRIA, and KITTI datasets), using only histogram of oriented gradient (HOG) and CIE-LUV [a color space composing of luminance (L) and two chrominance (UV) components by International Commission on Illumination] color features (HOG+LUV) as low-level features. One representative ExtFCF implementation achieves the best result compared with the current best traditional pedestrian detection methods on the Caltech dataset.