KEYWORDS: Homeland security, Signal processing, Sensors, Video, Cameras, Human-machine interfaces, Glasses, Defense and security, Computer security, Global Positioning System
Recent homeland security problems in various countries indicate that fixed surveillance systems at important places are not adequate enough. As the security threats take new dimensions in future, mobile smart security personnel wearing high-tech gear will form the basic infrastructure. See first, listen first, detect first, track first, communicate first with peers, assess the threat and coordinate with security head-quarters are the functions of high-tech gear. This paper proposes a high-tech gear involving (i) hands-free and obtrusion-free textile-based wearable microphone array to
capture users voice and interface with body-worn computer, (ii) microphone arrays embedded in textiles to listen and record others voices from a distance, (iii) miniature cameras embedded in the shirt to provide the user with omni vision (iv) wireless personal display as GUI hidden in textile or natural glasses, (v) GPS and body area network for positional awareness for information in the form of text or textile integrated, (vi) reconfigurable HW/SW for all the above functions configured in the form of a usual belt. The main focus of this paper is how to configure the high-tech gear with
all these sophisticated functions to disappear into the natural wearables of the user giving him normal look in the public.
This project is sponsored by Defence Science & Technology Agency, Ministry of Defence, Singapore. This paper covers multi-discipline technologies at system level, hence not possible to go into details of any subsystem. The main objective of this paper is to share our thoughts and get feedback. Progress and some critical design issues are discussed in this paper.
This paper proposes a pose estimation and frontal face detection
algorithm for face recognition. Considering it's application in a
real-world environment, the algorithm has to be robust yet
computationally efficient. The main contribution of this paper is
the efficient face localization, scale and pose estimation using
color models. Simulation results showed very low computational
load when compare to other face detection algorithm. The second
contribution is the introduction of low dimensional statistical
face geometrical model. Compared to other statistical face model
the proposed method models the face geometry efficiently. The
algorithm is demonstrated on a real-time system. The simulation
results indicate that the proposed algorithm is computationally
efficient.
In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.
A Vertical-Strip Least Mean Squared (VSLMS) algorithm is proposed to enhance the detection of small moving targets in IR image sequences. This algorithm is an improvement over the Two-Dimensional LMS (TDLMS), which is designed to detect small targets within highly correlated background of static images. This paper focuses on processing IR image sequences with different background features with layers of sky, sea and land clutter. The VSLMS uses multiple LMS modules and a different scanning method to process individual lines in the IR image sequences. Simulation results show successful enhancement of very small targets in an IR mage sequence.
Robust, real-time, user-friendly, non-restrictive and fully automatic natural like human computer interfaces are required to move away from the present machine-empowered-technologies to future human-empowered-technologies (HET). As one of HET interface technologies, this paper presents a cost-effective stereo face detection and tracking of facial features for determining facial pose. The object features are extracted using max-median filters and a progressive threshold algorithm, the face is verified on 'prominent feature configuration template.' Once face is confirmed, the features are tracked using dynamic programming filter. The results are impressive. Video clips would be shown during presentation in the symposium.
This paper presents a multi-mode fusion algorithm for detection and tracking of dim, point-like target. The key contribution of this paper includes the effective fusion approach to harvest the advantages and complement the disadvantages of various algorithms using conditional voting. From qualitative analysis, these algorithms are separated into two classes, i.e. main and supporting algorithms. In the multi-mode fusion algorithm high confidence is placed on the main algorithms with supporting algorithms used to further reduce the false alarm. The main algorithms trigger a voting process and detection is confirmed true if any of the supporting algorithms report detection. The multi-modal fusion algorithm has lower false alarm and moderate true detection rate compared to any individual algorithm namely, Triple Temporal Filter, Frame Differencing, Continuous Wavelet Transform, Max-median and 2-D Mexican hat filter. Besides, a novel variability filter is proposed to remove strong glint thus reduces false alarm. Kalman filter is used to track the detected targets. A novel track decision algorithm to continue or
terminate the track when target disappears is proposed. Prior knowledge of target in Kalman filter is fed forward to an Adaptive 3-D Matched filter to improve the performance. Three sets of real-world infrared image sequences with very different background and target characteristics were used to test the robustness of the multi-modal fusion algorithm. The algorithm performs satisfactorily in all the image sequences. Video clips will also be presented.
KEYWORDS: Target detection, Clouds, Detection and tracking algorithms, Signal processing, Digital filtering, Signal detection, Statistical analysis, 3D acquisition, Electronic filtering, Optical filters
The problem of detecting small target in IR imagery has attracted much research effort over the past few decades. As opposed to early detection algorithms which detect targets spatially in each image and then apply tracking algorithm, more recent approaches have used multiple frames to incorporate temporal as well as spatial information. They often referred to as track before detect algorithms. This approach has shown promising results particularly for detection of dim point-like targets. However, the computationally complexity has prohibited practical usage for such algorithms. This paper presents an adaptive, recursive and computation efficient detection method. This detection algorithm updates parameters and detects occurrence of targets as new frame arrived without storing previous frames, thus achieved recursiveness. Besides, the target temporal intensity change is modeled by two Gaussian distribution with different mean and variance. The derivation of this generalized model has taken account of the wide variation of target speed, therefore detects wider range of targets.
Detection and tracking of low-observable moving targets against heavy clutter in a sequence of infrared images is an important research area. The focus of research in this area is to reliably pick up the most potential targets, track the targets with varying speed and direction, and at the same time reduce the false alarm rate to an acceptable level. However, there is no single method that works equally well in all situations. This paper presents an integrated algorithm based on area-correlation tracker (ACT) and Kalman filter for improving ACT performance for targets with varying speed and direction. Divergence and loss of target when the target is stationary are the two typical problems associated with ACT. In our algorithm, we propose to overcome these shortcomings by introducing an online procedure for updating (or not updating in the case of occlusions) the reference template, in conjunction with linear predictions by using a Kalman filter.
Temporal profiles of point-like dim targets and the extended cloud pixels provide useful information in detecting the target. Among the recent methods that utilize temporal profile of pixel in detecting point target are Triple Temporal Filter (TTF) and Continuous Wavelet Transform (CWT). TTF uses two damped sinusoidal filters, an exponential averaging filter with six appropriate coefficients to deal with different aspects of clutter. TTF is recursive and efficient in detecting point targets without applying any threshold techniques. The performance of CWT is comparable to TTF but all the frames in a sequence need to be stored. Therefore it is computationally complex algorithm.
Temporal profiles of point-like dim targets and the extended cloud pixels provide useful information in detecting the targets. Among the recent methods that utilize temporal profile of pixel in detecting point target are Triple Temporal Filter (TTF) and Continuous Wavelet Transform (CWT). TTF uses two damped sinusoidal filters, an exponential averaging filter with six appropriate coefficients to deal with different aspects of clutter. TTF is recursive and efficient in detecting point targets without applying any threshold techniques. The performance of CWT is comparable to TTF but all the frames in a sequence need to be stored. Therefore it is computationally complex algorithm.
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