Classification of humans and animals using an infrared profiling sensor

Chari, Srikant; Halford, Carl E.; Jacobs, Eddie L.; Smith, Forrest A.; Brown, Jeremy B.; Russomanno, David J.

doi:10.1117/12.819885

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…We have found that simple characteristics and Bayesian classifiers perform remarkably well for human, animal, and vehicle discrimination. 8 For example, the maximum height and width of the profile combined with a naive Bayesian classifier typically yield correct membership probabilities greater than 95%, consistent with the results from the near IR-beam device.…”

Section: Continued On Next Pagesupporting

confidence: 57%

Profiling sensors for border and perimeter security

Jacobs¹

2009

SPIE Newsroom

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A novel intrusion detector offers several advantages compared with traditional methods, including a simpler design and improved levels of discrimination.Border and perimeter sensing is key to addressing concerns about national security and criminal activity. In addition, due to the vastness of national borders and the human resources needed to patrol even modest-sized areas, detection systems must be automated. Finally, for environmental and operational reasons, sensor systems must be able to communicate at low power or bandwidth, and to keep false alarms to a minimum.Simple pyroelectric motion detectors, such as those used in automatic light and home security systems, meet many of these requirements but produce too many false alarms. Imaging sensors provide a high level of discrimination, but they typically require human interaction as well as transmission of the image, which is costly in terms of bandwidth. We have been pursuing an approach that lies somewhere in between motion detectors and imaging sensors. Using a small and sparse array of detectors, we sense a minimal set of features from a moving object that enables reliable discrimination of humans, animals, and vehicles.Our approach originated with a concept put forward by Ron Sartain of the US Army Research Laboratory. 1 He reasoned that useful information about traffic along a path could be derived simply by determining the profile or silhouette of the objects that traverse it. It was hoped as well that critical discrimination could be obtained automatically. Prior efforts had shown that a great deal of information was available from shape alone. 2 To investigate this idea further, we constructed a simple sensor consisting of two vertical posts populated with a sparse array of near-IR transmitters and receivers 3, 4 (see Figure 1) that produces a series of parallel near-IR beams between the posts. As an object passes through, each part of it breaks one of the beams. Over time, the output of all the transmitters and receivers forms a crude image representing the profile of the object. This device was used to collect profiles of people, animals, and other objects. Several straightforward classification algorithms were then trained, tested, and evaluated using the data. We achieved better than 95% correct classification of our major categories.The near-IR beam prototype proved the concept of a profiling sensor, but it has many drawbacks in terms of deployment. We decided on a passive-IR approach as a viable alternative. [5][6][7] Pyroelectric-IR (PIR) devices were an obvious choice for detectors because they need no cryogenic cooling and exist in both single-device and array form. To help find the best configuration, we developed a detailed video simulation that produces output from arbitrary configurations of PIR arrays. We also used the simulation to account for the effects of lenses or mirrors for focusing. Figure 2 presents an example output.We are currently testing a prototype sensor constructed by the US Army Night Vision and Electronics Sensors Dir...

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Section: Continued On Next Pagesupporting

confidence: 57%

Profiling sensors for border and perimeter security

Jacobs¹

2009

SPIE Newsroom

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“…Further, w and w 0 can be scaled so that the distance of the hyper-plane from the nearest points in class 1 and class 2 is set to unity. The margin is then given by (6). Further, if x belongs to class 1, then w T x+w 0 ≥ 1 and if x belongs to class 2, then w T x+w 0…”

Section: Support Vector Machinesmentioning

confidence: 98%

“…For open area surveillance, passive profiling sensors (the detected radiation is generated by the object) are more effective 5 . In our previous work we simulated a profiling sensor using a LWIR camera 6,7 . We showed that range independent features based on height and width, extracted from the sensor, can be used for classifying humans and animals with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Range and velocity independent classification of humans and animals using a profiling sensor

et al. 2010

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This paper presents object profile classification results using range and speed independent features from an infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. Field data collected near the US-Mexico border to yield profiles of humans and animals is reported. Range and speed independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test three classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set all three algorithms achieve classification rates of over 98%. The field data is also used to validate our prior data collections from more controlled environments.

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“…The linear pyroelectric array profiling sensor is a passive system that uses long wave infrared (LWIR) detectors [2,5,21]. Unlike any of the N-IR profiling sensors described in Section 2, the pyroelectric approach does not generate radiation.…”

Section: Alternative Profiling Sensor Modelsmentioning

confidence: 99%

“…A similar thresholding technique is used to convert the grayscale image to a binary black and white image. Figure 17 shows a block diagram of the profile generation process leading to the binary image used for classification from a passive IR sensor that emulates a profiling sensor by extracting a single column of detectors [2]. …”

Section: Alternative Profiling Sensor Modelsmentioning

confidence: 99%

Advancing Profiling Sensors with a Wireless Approach

Galvis

Russomanno

2012

Sensors

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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.

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Classification of humans and animals using an infrared profiling sensor

Cited by 12 publications

References 8 publications

Profiling sensors for border and perimeter security

Profiling sensors for border and perimeter security

Range and velocity independent classification of humans and animals using a profiling sensor

Advancing Profiling Sensors with a Wireless Approach

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