CONCEALED WEAPON DETECTION

USING DIGITAL IMAGE PROCESSING

INTRODUCTION:

The ability to detect a concealed weapon under clothing at a standoff

distance of 10 feet and longer can greatly enhance potential threat detection in situations

such as crowd control, school safety, and airport security and etc. al. The detection can be

outdoor and/or indoor. The detector can be handheld for portable short-range detection,

or fixedhehicle-mounted for scanning a crowd. Because of the clothing, visible detection

of a concealed weapon is impossible. Detection with an infrared thermal detector is also

not practical because of the very small temperature contrast of a concealed weapon under

clothing and especially heavy clothing. Several techniques are currently being

investigated under National Institute of Justice contracts: Ultrasound echo detection of

concealed weapon through clothing, microwave signal reflection through clothing and

body to detect concealed weapon, passive millimeter wave imaging to detect temperature

contrast of the concealed weapon with the body through clothing, and the millimeter

wave radar (active) imaging through clothing. There was also a concept of using a

magnetometer array for ferric metal weapon detection. Each of the above techniques has

its advantages and drawbacks. This research project under National Institute of Justice,

Office of Science and Technology sponsorship was to investigate an active millimeter

wave imaging techniques and technologies that could enable the design of handheld

detectors with a fast image scan time frame to detect concealed weapon at a minimum

range of 10 feet. Our research results also indicated further work to be conducted in

conjunction with the motion of the person, and the potential limitations of the technique.

Active millimeter-wave imaging of subjects for the purpose of non-invasive search has

many attractive aspects: it is possible to produce a reasonable small beam with an antenna

compatible with portable or handheld use, system performance is not affected by weather

or indoor operation, and fast frame times with good signal to noise ratios can be obtained

with low power non-destructive radiation. Though such a system has many attractive

aspects, millimeter-wave radar images show a marked difference fiom their visible

cousins. The signal and image processing challenges arising fiom specular reflection,

glint, phase cancellation, and environmental clutter require a good way to gather data in a

variety of environments. CHANG Industry Inc. has developed a portable fast scanning

millimeter-wave radar imaging system that will be able to gather data on a variety of

subjects in a variety of roles. The system is suitable for tripod mounting and data can be

visualized remotely on a laptop or desktop computer system. This form factor allows the

system to be deployed as an entry portal scanner, as an abandoned bag scanner, or as a

patrol car mounted system. This flexibility will allow the collection of a large amount of

data fiom a variety of missions leading to solution of the signal processing problems that

will allow a handheld, or a small commercially available system to be produced. The

selection of the 94GHz band was driven by the compatibility of the required antenna size

With handheld or small portable operation. Using only a ten-inch antenna it is possible to

generate a beam spot size of less than two inches at a ten-foot standoff distance. A spot

size of less than one inch is achievable at five feet, which is compatible with abandoned

bag search. This spot size yields an image of metallic and possibly non-metallic targets

with a reasonable resolution that can be over sampled for image processing and target

identification purposes. By using an active system the

Image quality is not dependent on “temperature contrast” or the presence of cold sky

radiation and alleviates the need for an extremely high sensitivity receiver. Using less

than 1 mW of radiated power there is sufficient signal to noise ratio that a fast frame time

is possible by scanning a single beam thereby removing the need for an expensive sensor

array. This small power output is well below FCC health guidelines for radiated

exposure’ yet still easily penetrates many layers of heavy clothing. Boot and detected by

a 94GHz imaging radar initially developed with a circular scan technique, the radar.

Image with and without the handgun clearly indicates that the concealed handgun can be

detected even through heavy leather. The choice of an active system suffers from glint

problems and a loss of signal or image distortion from constructive and destructive

interference from multiple strong reflectors in the image. Isolating returns from target

weapons and bony portions of the human body is also a challenge. These problems can be

mitigated by the fine range resolution that is provided by an active system to allow

targets to be localized in space away from clutter. As a result of a platform that can

produce good data signal processing solutions should be able to be developed to solve the

challenges of clutter and target identification. We present an overview of signal and

image processing techniques developed for the concealed weapon detection (CWD)

application. The signal/image processing chain is described and the tasks include image

denoising and enhancement, image registration and fusion, object segmentation, shape

description, and weapon recognition. Finally, a complete CWD example is presented for

illustration

INFRARED IMAGING:

Infrared Thermography, thermal imaging, thermographic imaging,

or thermal video, is a type of infrared imaging science. Thermographic cameras detect

radiation in the infrared range of the electromagnetic spectrum (roughly 900–14,000

nanometers or 0.9–14 µm) and produce images of that radiation. Since infrared radiation

is emitted by all objects based on their temperatures, according to the black body

radiation law, thermography makes it possible to "see" one's environment with or without

visible illumination. The amount of radiation emitted by an object increases with

temperature, therefore thermography allows one to see variations in temperature (hence

the name). When viewed by thermographic camera, warm objects stand out well against

cooler backgrounds; humans and other warm-blooded animals become easily visible

against the environment, day or night. As a result, thermography's extensive use can

historically be ascribed to the military and security services. It is important to note that

thermal imaging displays the amount of infrared energy emitted, transmitted, and

reflected by an object. Because of this, it is quite difficult to get an accurate temperature

of an object using this method.

Thus, Incident Energy = Emitted Energy + Transmitted Energy + Reflected Energy

where Incident Energy is the energy profile when viewed through a thermal imaging

device, Emitted Energy is generally what is intended to be measured, Transmitted Energy

is the energy that passes through the subject from a remote thermal source, and Reflected

Energy is the amount of energy that reflects off the surface of the object from a remote

thermal source.

If the object is radiating at a higher temperature than its surroundings, then power transfer

will be taking place and power will be radiating from warm to cold following the

principle stated in the Second Law of Thermodynamics. So if there is a cool area in the

thermograph, that object will be absorbing the radiation emitted by the warm object. The

ability of both objects to emit or absorb this radiation is called emissivity (see below). In

outdoor environments, convective cooling from wind may also need to be considered

when trying to get an accurate temperature reading. IR film is sensitive to infrared

radiation in the 250-500 °C range, while the range of thermography is approximately -50

°C to over 2,000 °C. So for an IR film to show something it must be over 250 °C or be

reflecting infrared radiation from something that is at least that hot. Night vision infrared

devices image in the non-thermal range of infrared (Near IR) just beyond the visual

spectrum, and can see emitted or reflected NIR in complete visual darkness. Starlight-

type night vision devices generally only magnify ambient light

P M W IMAGING SENSORS:

FIRST GENERATION:

Passive millimeter wave (MMW) sensors measure the apparent temperature through the

energy that is emitted or reflected by sources. The output of the sensors is a function of

the emissive of the objects in the MMW spectrum as measured by the receiver.

Clothing penetration for concealed weapon detection is made possible by MMW sensors

due to the low emissive and high reflectivity of objects

SECOND GENERATION:

• Recent advances in MMW sensor technology have led to video-rate (30frames/s) MW

cameras.

• This system collects up to 30 frames/s of MMW data.

• By fusing passive MMW image data and its corresponding infrared (IR) or electro-

optical (EO) image, more complete information can be obtained; the information can then

be utilized to facilitate concealed weapon detection.

• Fusion of an IR image revealing a concealed weapon and its corresponding MMW

image has been shown to facilitate extraction of the concealed weapon. This is illustrated

in the example given in following figure 3a) Shows an image taken from a regular CCD

camera, and Figure3b) shows a corresponding MMW image. If either one of these two

images alone is presented to a human operator, it is difficult to recognize the weapon

Concealed underneath the rightmost person’s clothing. If a fused image as shown in

Figure 3c) is presented, a human operator is able to respond with higher accuracy.

IMAGING PROCESSING ARCHITECTURE:

•An image processing architecture for CWD is shown in Figure 4.The input can be

Multi sensor (i.e., MMW + IR, MMW + EO, or MMW + IR + EO) data or only the

MMWdata.

•In the latter case, the blocks showing registration and fusion can be removed from

Figure 4. The output can take several forms.

• It can be as simple as a processed image/video sequence displayed on a screen; a cued

display where potential concealed weapon types and locations are highlighted with

associated confidence measures; a “yes,” “no,” or “maybe” indicator; or : An imaging

Processing architecture

overview for CWD

WAVELET APPROACHS FOR PRE PROCESSING:

• The preprocessing steps considered in this section include enhancement and filtering for

the removal of shadows, wrinkles, and other artifacts.

• When more than one sensor is used, preprocessing must also include registration and

fusion procedures.

i) IMAGE DENOISING & ENHANCEMENT THROUGH WAVELETS:

•In this section, we describe a technique for simultaneous noise suppression and object

enhancement of passive MMW video data and show some mathematical results.

•Denoising of the video sequences can be achieved temporally or spatially. First,

temporal denoising is achieved by motion compensated filtering, which estimates the

motion trajectory of each pixel and then conducts a 1-D filtering along the trajectory.

•This reduces the blurring effect that occurs when temporal filtering is performed without

regard to object motion between frames.

ii) CLUTTER FILTERING:

• Clutter filtering is used to remove unwanted details (shadows, wrinkles, imaging

artifacts, etc.) that are not needed in the final image for human observation, and can

adversely affect the performance of the automatic recognition stage.

• This helps improve the recognition performance, either operator assisted or automatic.

For this purpose, morphological filters have been employed.

III) REGISTRATION OF MULTI SENSOR IMAGES:

• The first step toward image fusion is a precise alignment of images (i.e., image

registration).

• Here, we describe a registration approach for images taken at the same time from

different but nearly collocated (adjacenand parallel) sensors based on the maximization

of mutual information (MMI) criterion. MMI states that two images are registered when

their mutual information (MI) reaches its maximum value.

For the registration of IR images and the corresponding MMW images of the first

generation. At the first stage, two human silhouette extraction algorithms were

developed, followed by a binary correlation to coarsely register the two images.

The purpose was to provide an initial search point close to the final solution

For the second stage of the registration algorithm based on the MMI criterion. In

this manner, any local optimizer can be employed to maximize the MI measure.

One registration result obtained by this approach is illustrated through the

example

IMAGE DECOMPOSITION:

•The most straightforward approach to image fusion is to take the average of the source

images, but this can produce undesirable results such as a decrease in contrast.

•Many of the advanced image fusion methods involve multi resolution image

decomposition based on the wavelet transform

AUTOMATIC WEAPON DETECTION:

After preprocessing, the images/video sequences can be displayed for operator-

assisted weapon detection or fed into a weapon detection module for automated

weapon detection.

Toward this aim, several steps are required, including object extraction, shape

description, and weapon recognition.

SEGMENTATION FOR OBJECT EXTRACTION:

Object extraction is an important step towards automatic recognition of a weapon,

regardless of whether or not the image fusion step is involved.

It has been successfully used to extract the gun shape from the fused IR and

MMW images.

This could not be achieved using the original images alone.

Computes multiple important thresholds of the image data in the automatic

threshold computation (ATC) stage for 1) regions with distinguishable intensity

levels, and 2) regions with close intensity levels.

Regions with distinguishable intensity levels have multi modal histograms,

Where as regions with close intensity levels have overlapping histograms.

The thresholds from both cases are fused to form the set of important

Thresholds in the scene.

At the output of the ATC stage, the scene is quantized for each threshold

Value to obtain data above and below

CONCLUSIONS AND RECOMMANDATIONS:

Even though active radar images of concealed weapons have some drawbacks such as

glint and specular reflection or artifacts such as coherent interference these problems

should be able to be overcome or used to our advantage once enough data is available to

tackle the problem fully. In addition to building up a library of data on radar returns from

body parts nearby clutter targets can already be isolated by range resolution that could be

increased by another 50%, the concealing clothing itself can reduce glint and specular

reflections, and positional jitter and large frequency

Shifts show some promise in reducing or making use of interference effects.

Using a millimeter wave 94GHz radar for concealed weapon imaging has the following

Advantages:

Relatively small antenna size to generate a narrow antenna beam for sharp lateral

resolution in

A form factor compatible with portable or handheld use.

Very small transmitted power at 94GHz making handheld portable detector

possible

Very small transmitted power at 94GHz will be cause damage to the human body

and eyes

Ability to penetrate relatively heavy clothing including leather

Ability to generate consistent images indoors and outdoors independent of

weather

Potential low final system cost

Three dimensional data for better target identification

Generates data that is machine decipherable so should not raise privacy concerns

When compared to food, clothing, shoes etc. shrapnel producing metal objects in

abandoned bags should show up easily even at long stand off distances. The potential

usefulness for bag search could be great. Since the bag is stationary, by obtaining

repeated image scans of the three-dimensional data sets the expert information needed to

determine the existence of the pipe bomb could be greatly reduced.

For future active radar imaging concealed weapon detection, the following

recommendations are provided for consideration:

Development of faster scan to counter body motion

Development of fast scanning, rugged and light gimbal system

Miniaturization of the 94GHz radar frontend based on low cost MMIC electronics

Collection of a large number of concealed weapon radar signal data through different

possible clothing types to establish the data base for target recognition software

development Extensive analysis of concealed weapon data sets to produce robust target

isolation and identification algorithms. Therefore, we conclude that further development

effort is necessary to bring this technology to its maturity and to the law enforcement

market place.