Abstract

We introduce a new approach for learning part-based ob ject detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation proce- dure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using a new feature selection algorithm for linear SVM, termed Predictive Feature Selection (PFS), which is governed by weight prediction. The algorithm makes it possi- ble to choose from O(106 ) features in an efficient but accurate manner. We analyze the validity and behavior of PFS and empirically demon- strate its speed and accuracy advantages over relevant competitors. We present an empirical evaluation of our method on three human detec- tion datasets including the current de-facto benchmarks (the INRIA and Caltech pedestrian datasets) and a new challenging dataset of children images in difficult poses. The evaluation suggests that our approach is on a par with the best current methods and advances the state-of-the-art on the Caltech pedestrian training dataset.