Tracking Motion and Proxemics using Thermal-sensor Array
This work addresses privacy-preserving indoor tracking for applications like education and health monitoring, but it is incremental as it applies existing methods (cross-correlation and SVM) to new thermal data.
The paper tackled indoor tracking and group proxemics modeling using an 8x8 thermal-sensor array to preserve privacy, achieving motion direction inference and human instance counting from approximately 902 scenes.
Indoor tracking has all-pervasive applications beyond mere surveillance, for example in education, health monitoring, marketing, energy management and so on. Image and video based tracking systems are intrusive. Thermal array sensors on the other hand can provide coarse-grained tracking while preserving privacy of the subjects. The goal of the project is to facilitate motion detection and group proxemics modeling using an 8 x 8 infrared sensor array. Each of the 8 x 8 pixels is a temperature reading in Fahrenheit. We refer to each 8 x 8 matrix as a scene. We collected approximately 902 scenes with different configurations of human groups and different walking directions. We infer direction of motion of a subject across a set of scenes as left-to-right, right-to-left, up-to-down and down-to-up using cross-correlation analysis. We used features from connected component analysis of each background subtracted scene and performed Support Vector Machine classification to estimate number of instances of human subjects in the scene.