A robust system for counting people using an infrared sensor and a camera

In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner. © 2015 Elsevier B.V. All rights reserved

Doctor of Philosophy

dissertationFingernail imaging is a method of sensing finger force using the color patterns on the nail and surrounding skin. These patterns form as the underlying tissue is compressed and blood pools in the surrounding vessels. Photos of the finger and surrounding skin may be correlated to the magnitude and direction of force on the fingerpad. An automated calibration routine is developed to improve the data-collection process. This includes a novel hybrid force/position controller that manages the interaction between the fingerpad and a flat surface, implemented on a Magnetic Levitation Haptic Device. The kinematic and dynamics parameters of the system are characterized in order to appropriately design a nonlinear compensator. The controller settles within 0.13 s with less than 30% overshoot. A new registration A new registration technique, based on Active Appearance Models, is presented. Since this method accounts for the variation inherent in the finger, it reduces registration and force prediction errors while removing the need to tune registration parameters or reject unregistered images. Modifications to the standard model are also investigated. The number of landmark points is reduced to 25 points with no loss of accuracy, while the use of the green channel is found to have no significant effect on either registration or force prediction accuracy. Several force prediction models are characterized, and the EigenNail Magnitude Model, a Principal Component Regression model on the gray-level intensity, is shown to fit the data most accurately. The mean force prediction error using this prediction and modeling method is 0.55 N. White LEDs and green LEDs are shown to have no statistically significant effect on registration or force prediction. Finally, two different calibration grid designs are compared and found to have no significant effect. Together, these improvements prepare the way for fingernail imaging to be used in less controlled situations. With a wider range of calibration data and a more robust registration method, a larger range of force data may be predicted. Potential applications for this technology include human-computer interaction and measuring finger interaction forces during grasping experiments