WNet: Joint Multiple Head Detection and Head Pose Estimation from a Spectator Crowd Image

Abstract

Crowd image analysis has various application areas such as surveillance, crowd management and augmented reality. Existing techniques can detect multiple faces in a single crowd image, but small head/face size and additional non facial regions in the head bounding box makes the head detection (HD) challenging. Additionally, in existing head pose estimations (HPE) of multiple heads in an image, individual cropped head image is passed through a network one by one, instead of estimating poses of multiple heads at the same time. The proposed WNet, performs both HD and HPE jointly on multiple heads in a single crowd image, in a single pass. Experiments are demonstrated on the spectator crowd S-HOCK dataset and results are compared with the HPE benchmarks. WNet proposes to use lesser number of training images compared to number of cropped images used by benchmarks, and does not utilize transferred weights from other networks. WNet not just performs HPE, but joint HD and HPE efficiently i.e. accuracy for more number of heads while depending on lesser number of testing images, compared to the benchmarks.