Coded apertures for efficient pyroelectric motion tracking

Compressive sensing in the EO/IR

We investigate the utility of compressive sensing (CS) to electro-optic and infrared (EO/IR) applications. We introduce the field through a discussion of historical antecedents and the development of the modern CS framework. Basic economic arguments (in the broadest sense) are presented regarding the applicability of CS to the EO/IR and used to draw conclusions regarding application areas where CS would be most viable. A number of experimental success stories are presented to demonstrate the overall feasibility of the approaches, and we conclude with a discussion of open challenges to practical adoption of CS methods.

Human movement detection and identification using pyroelectric infrared sensors

Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape and gait. In this paper, we present an empirical study of human movement detection and identification using a set of PIR sensors. We have developed a data collection module having two pairs of PIR sensors orthogonally aligned and modified Fresnel lenses. We have placed three PIR-based modules in a hallway for monitoring people; one module on the ceiling; two modules on opposite walls facing each other. We have collected a data set from eight subjects when walking in three different conditions: two directions (back and forth), three distance intervals (close to one wall sensor, in the middle, close to the other wall sensor) and three speed levels (slow, moderate, fast). We have used two types of feature sets: a raw data set and a reduced feature set composed of amplitude and time to peaks; and passage duration extracted from each PIR sensor. We have performed classification analysis with well-known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from a single PIR sensor of each of the three modules, we could achieve more than 92% accuracy in classifying the direction and speed of movement, the distance interval and identifying subjects. We could also achieve more than 94% accuracy in classifying the direction, speed and distance and identifying subjects using the reduced feature set extracted from two pairs of PIR sensors of each of the three modules.

Fiber-optic localization by geometric space coding with a two-dimensional gray code

With the objective of monitoring motion within a room, we segment the two-dimensional (2D) floor space into discrete cells and encode each cell with a binary code word generated by a fiber. We design a set of k-neighbor-local codes to localize an extended object and, particularly when k = 2, employ a 2D gray code to localize a human by tracking his or her footsteps. Methods for implementing the codes in a fiber web are discussed, and we demonstrate the experimental result with the fiber mat. The observed system performance confirms the theoretical analysis. The space coding technique is a promising low-cost candidate not only for human tracking but also for other applications such as human gait analysis.

Reference structure tomography

Reference structure tomography (RST) uses multidimensional modulations to encode mappings between radiating objects and measurements. RST may be used to image source-density distributions, estimate source parameters, or classify sources. The RST paradigm permits scan-free multidimensional imaging, data-efficient and computation-efficient source analysis, and direct abstraction of physical features. We introduce the basic concepts of RST and illustrate the use of RST for multidimensional imaging based on a geometric radiation model.