Advancing Profiling Sensors with a Wireless Approach

Abstract

The notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.

Compressive Imaging of Moving Object Based on Linear Array Sensor

“…In practical situations, objects that are observed moving across borders or uninhabited regions could be either human or animal, and there are considerable differences between their profiles [1,2]. Large numbers of research results have indicated that it is feasible to determine which objects are human and which are animal based on these differences, but the current research is generally concerned with the acquisition and recognition of the object profile image [3][4][5][6][7][8]. For image acquisition, the most common method is the use of a linear array sensor to acquire the moving object image [9][10][11], but the acquired image is relatively simple and cannot show the object in greater detail, meaning that it is difficult to distinguish between similar actions on different objects.…”