  Head detection in the indoor video is an essential component of building
occupancy detection. While deep models have achieved remarkable progress in
general object detection, they are not satisfying enough in complex indoor
scenes. The indoor surveillance video often includes cluttered background
objects, among which heads have small scales and diverse poses. In this paper,
we propose Motion-aware Pseudo Siamese Network (MPSN), an end-to-end approach
that leverages head motion information to guide the deep model to extract
effective head features in indoor scenarios. By taking the pixel-wise
difference of adjacent frames as the auxiliary input, MPSN effectively enhances
human head motion information and removes the irrelevant objects in the
background. Compared with prior methods, it achieves superior performance on
the two indoor video datasets. Our experiments show that MPSN successfully
suppresses static background objects and highlights the moving instances,
especially human heads in indoor videos. We also compare different methods to
capture head motion, which demonstrates the simplicity and flexibility of MPSN.
To validate the robustness of MPSN, we conduct adversarial experiments with a
mathematical solution of small perturbations for robust model selection.
Finally, for confirming its potential in building control systems, we apply
MPSN to occupancy counting. Code is available at
https://github.com/pl-share/MPSN.
