Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture

This paper presents a method that can accurately detect heads especially small heads under indoor scene. To achieve this, we propose a novel Feature Refine Net (FRN) and a cascaded multi-scale architecture. FRN exploits the multiscale hierarchical features created by deep convolutional neural networks. Proposed channel weighting method enables FRN to make use of features alternatively and effectively. To improve the performance of small head detection, we propose a cascaded multi-scale architecture which has two detectors. One called global detector is responsible for detecting large objects and acquiring the global distribution information. The other called local detector is specified for small objects detection and makes use of the information provided by global detector. Due to the lack of head detection datasets, we have collected and labeled a new large dataset named SCUT-HEAD that includes 4405 images with 111251 heads annotated. Experiments show that our method has achieved state-of-art performance on SCUT-HEAD.