A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been shown to have advantages over frequency domain implementations of the Optimal Trade-Off Maximum Average Correlation Height (OR-MACH) filter as it can be made locally adaptive to spatial variations in the input image background clutter and normalized for local intensity changes. This enables the spatial domain implementation to be resistant to illumination changes. The Affine Scale Invariant Feature Transform (ASIFT) is an extension of previous feature transform algorithms; its features are invariant to six affine parameters which are translation (2 parameters), zoom, rotation and two camera axis orientations. This results in it accurately matching increased numbers of key points which can then be used for matching between different images of the object being tested. In this paper a novel approach will be adopted for enhancing the performance of the spatial correlation filter (SPOT MACH filter) using ASIFT in a pre-processing stage enabling fully invariant object detection and recognition in images with geometric distortions. An optimization criterion is also be developed to overcome the temporal overhead of the spatial domain approach. In order to evaluate effectiveness of algorithm, experiments were conducted on two different data sets. Several test cases were created based on illumination, rotational and scale changes in the target object. The performance of correlation algorithms was also tested against composite images as references and it was found that this results in a well-trained filter with better detection ability even when the target object has gone through large rotational changes.
A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. The main concern for using the SPOT-MACH is its computationally intensive nature. However in the past enhancements techniques were proposed for the SPOT-MACH to make its execution time comparable to its frequency domain counterpart. In this paper a novel approach is discussed which uses VANET parameters coupled with the SPOT-MACH in order to minimise the extensive processing of the large video dataset acquired from the Pakistan motorways surveillance system. The use of VANET parameters gives us an estimation criterion of the flow of traffic on the Pakistan motorway network and acts as a precursor to the training algorithm. The use of VANET in this scenario would contribute heavily towards the computational complexity minimization of the proposed monitoring system.
A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously
developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive
to spatial variations in the input image background clutter and normalised for local intensity changes. In this paper we
compare the performance of the spatial domain (SPOT-MACH) filter to the widely applied data driven technique known
as the Scale Invariant Feature Transform (SIFT). The SPOT-MACH filter is shown to provide more robust recognition
performance than the SIFT technique for demanding images such as scenes in which there are large illumination
gradients. The SIFT method depends on reliable local edge-based feature detection over large regions of the image plane
which is compromised in some of the demanding images we examined for this work. The disadvantage of the SPOTMACH
filter is its numerically intensive nature since it is template based and is implemented in the spatial domain.
An improvement to the wavelet-modified Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter
with the use of the Rayleigh distribution filter is proposed. The Rayleigh distribution filter is applied to the OT-MACH
filter to provide a sharper low frequency cut-off than the Laplacian of Gaussian based wavelet filter that has been
previously reported to enhance OT-MACH filter performance. Filters are trained using a 3D CAD model and tested on
the corresponding real target object in high clutter environments acquired from a Forward Looking Infra Red (FLIR)
sensor. Comparative evaluation of the performance of the original, wavelet and Rayleigh modified OT-MACH filter is
reported for the recognition of the target objects present within the thermal infra-red image data set.
A speed enhanced space variant correlation filer which has been designed to be invariant to change in orientation and
scale of the target object but also to be spatially variant, i.e. the filter function becoming dependant on local clutter
conditions within the image. The speed enhancement of the filter is due to the use of optimization techniques employing
low-pass filtering to restrict kernel movement to be within regions of interest. The detection and subsequent
identification capability of the two-stage process has been evaluated in highly cluttered backgrounds using both visible
and thermal imagery acquired from civil and defense domains along with associated training data sets for target detection
and classification. In this paper a series of tests have been conducted in multiple scenarios relating to situations that pose
a security threat. Performance matrices comprised of peak-to-correlation energy (PCE) and peak-to-side lobe ratio (PSR)
measurements of the correlation output have been calculated to allow the definition of a recognition criterion. The
hardware implementation of the system has been discussed in terms of Field Programmable Gate Array (FPGA) chipsets
with implementation bottle necks and their solution being considered.
A wavelet-modified frequency domain Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter has
been trained using 3D CAD models and tested on real target images acquired from a Forward Looking Infra Red (FLIR)
sensor. The OT-MACH filter can be used to detect and discriminate predefined targets from a cluttered background. The
FLIR sensor extends the filter's ability by increasing the range of detection by exploiting the heat signature differences
between the target and the background. A Difference of Gaussians (DoG) based wavelet filter has been use to improve
the OT-MACH filter discrimination ability and distortion tolerance. Choosing the right standard deviation values of the
two Gaussians comprising the filter is critical. In this paper we present a new technique for auto adjustment of the DoG
filter parameters driven by the expected target size. Tests were carried on images acquired by the Apache AH-64
helicopter mounted FLIR sensor, results showing an overall improvement in the recognition of target objects present
within the IR images.
A frequency domain implementation of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter
has been optimized to classify target vehicles acquired from a Forward Looking Infra Red (FLIR) sensor. The clutter
noise does not have a white spectrum and models employing the power spectral density of the background clutter require
a predefined threshold. A method of automatically adjusting the noise model in the filter by using the input image
statistical information has been introduced. Parameter surfaces for the remaining OT-MACH variables are calculated in
order to determine optimal operating conditions for the view independent recognition of vehicles in highly cluttered
FLIR imagery.
A space domain implementation of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter can
not only be designed to be invariant to change in orientation of the target object but also to be spatially variant, i.e. the
filter function becoming dependant on local clutter conditions within the image. Sequential location of the kernel in all
regions of the image does, however, require excessive computational resources. An optimization technique is discussed
in this paper which employs low-pass filtering to highlight the potential region of interests in the image and then restricts
the movement of the kernel to these regions to allow target identification. The detection and subsequent identification
capability of the two-stage process has been evaluated in highly cluttered backgrounds using both visible and thermal
imagery and associated training data sets. A performance matrix comprised of peak-to-correlation energy (PCE) and
peak-to-side lobe ratio (PSR) measurements of the correlation output has been calculated to allow the definition of a
recognition criterion. A feasible hardware implementation for potential use in a security application using the proposed
two-stage process is also described in the paper.
We propose a space variant Maximum Average Correlation Height (MACH) filter which can be locally modified
depending upon its position in the input frame. This can be used to detect targets in an environment from varying ranges
and in unpredictable weather conditions using thermal images. It enables adaptation of the filter dependant on
background heat signature variances and also enables the normalization of the filter energy levels. The kernel can be
normalized to remove a non-uniform brightness distribution if this occurs in different regions of the image. The main
constraint in this implementation is the dependence on computational ability of the system. This can be minimized with
the recent advances in optical correlators using scanning holographic memory, as proposed by Birch et al. [1]
In this paper we describe the discrimination abilities of the MACH filter against background heat signature variances and
tolerance to changes in scale and calculate the improvement in detection capabilities with the introduction of a nonlinearity.
We propose a security detection system which exhibits a joint process where human and an automated pattern
recognition system contribute to the overall solution for the detection of pre-defined targets.
Volume holographic correlators offer the ability to encode and compare thousands of templates in one operation.
Angle multiplexing of each individual template means the position of the correlation spot in the output plane
corresponds to the matching template. To be useful as a correlator the shift invariance must be restored by
scanning the input image. This can be achieved by implementing the input signal modulation on a high speed
SLM such as a MQW or DLP that is capable speeds in excess of 30kHz. The output correlation peak is read
out using a high-speed linear CCD camera. The Bragg angle affects the number of templates that can be
held on the hologram. However, this is not the same in both directions and this changes the correlator's shift
invariance ability in different scan directions. In this paper we investigate this and how it affects the correlator's
performance. This arrangement allows thousands of templates to be searched at video rate. The scanning nature
allows space domain correlation to be implemented. The system we describe offers the ability to pre-filter the
signal. We report on the results of a MACH filter implemented in a volume holographic correlator. The scanning
window allows some interesting pre-filtering to be performed, such normalisation and non-linear optimisation.
A moving space domain window is used to implement a Maximum Average Correlation Height (MACH) filter which
can be locally modified depending upon its position in the input frame. This enables adaptation of the filter dependant on
locally variant background clutter conditions and also enables the normalization of the filter energy levels at each step.
Thus the spatial domain implementation of the MACH filter offers an advantage over its frequency domain
implementation as shift invariance is not imposed upon it. The only drawback of the spatial domain implementation of
the MACH filter is the amount of computational resource required for a fast implementation. Recently an optical
correlator using a scanning holographic memory has been proposed by Birch et al [1] for the real-time implementation of
space variant filters of this type. In this paper we describe the discrimination abilities against background clutter and
tolerance to in-plane rotation, out of plane rotation and changes in scale of a MACH correlation filter implemented in the
spatial domain.
KEYWORDS: RGB color model, Binary data, Sensors, Detection and tracking algorithms, Distortion, Algorithm development, Cameras, Roentgenium, Information security, Intelligence systems
Baggage abandoned in public places can pose a serious security threat. In this paper a two-stage approach
that works on video sequences captured by a single immovable CCTV camera is presented. At first, foreground
objects are segregated from static background objects using brightness and chromaticity distortion parameters
estimated in the RGB colour space. The algorithm then locks on to binary blobs that are static and of 'bag' sizes;
the size constraints used in the scheme are chosen based on empirical data. Parts of the background frame and
current frames covered by a locked mask are then tracked using a 1-D (unwrapped) pattern generated using a
bi-variate frequency distribution in the rg chromaticity space. Another approach that uses edge maps instead of
patterns generated using the fragile colour information is discussed. In this approach the pixels that are part of
an edge are marked using a novel scheme that utilizes four 1-D Laplacian kernels; tracking is done by calculating
the total entropy in the intensity images in the sections encompassed by the binary edge maps. This makes the
process broadly illumination invariant. Both the algorithms have been tested on the iLIDS dataset (produced
by the Home Office Scientific Development Branch in partnership with Security Service, United Kingdom) and
the results obtained are encouraging.
We propose a novel space domain volume holographic correlator system. One of the limitations of
conventional correlators is the bandwidth limits imposed by updating the filter and the readout speed of
the CCD. The volume holographic correlator overcomes these by storing a large number of filters that
can be interrogated simultaneously. By using angle multiplexing, the match can be read out onto a high
speed linear array of sensors. A scanning window can be used to implement shift invariance, thus,
making the system operate like a space domain correlator.
The space domain correlation method offers an advantage over the frequency domain correlator in that
the correlation filter no longer has shift invariance imposed on it since the kernel can be modified
depending on its position. This maybe used for normalising the kernel or imposing some non-linearity
in an attempt to improve performance.
However, one of the key advantages of the frequency domain method is lost using this technique,
namely the speed of the computation. A large kernel space-domain correlation, performed on a
computer, will be very slow compared to what is achievable using a 4f optical correlator. We propose a
method of implementing this using the scanning holographic memory based correlator.
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