MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings
This work addresses occupancy detection in buildings for applications like building control systems, but it is incremental as it builds on existing deep learning methods with a focus on motion cues.
The paper tackles head detection in indoor surveillance videos, which is challenging due to cluttered backgrounds and small, diverse head poses, by proposing MPSN, a motion-aware pseudo Siamese network that leverages head motion information to enhance features and suppress static objects, achieving superior performance on two indoor video datasets.
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.