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In this paper a new method of detecting and tracking a human person in three dimensional space using audio and video data is proposed. A simple tracking system with two microphones and stereo vision is introduced. The audio information is resulting from the generalized cross correlation (GCC) algorithm, and the video information is extracted by the continuously adaptive mean shift (CAMshift) method...
In despite of successful implementation of iris recognition systems, noncooperative recognition is still remained as an unsolved problem. Unexpected behavior of the subjects and uncontrolled lighting conditions as the main aspects of noncooperative iris recognition result in blurred and noisy captured images. This issue can degrade the performance of iris recognition system. In this paper, to address...
In this paper, we employ the recent on-line boosting framework to fuse heterogeneous features for object detection and tracking in a video surveillance application. Detection and tracking are treated as a classification problem by an ensemble of weak classifiers built on heterogeneous feature types and updated on-line. We extend the on-line boosting framework by proposing an algorithm that builds...
This paper considers the notion of the immune system as an information processing system for the purpose of data fusion. As the study of the brain inspired the development of neural networks, the immune system has inspired the development of a wide variety of algorithms. In this position paper, we argue how the various processes of molecular interaction and fusion in the biological immune system can...
The challenge of modern sensor systems is besides the tracking of targets more and more their classification. The knowledge of the target class has significant influence on the identification, threat evaluation and weapon assignment process of large systems. Especially, considering new types of threats in anti asymmetric warfare the knowledge of a target class has an important drawback. Also the target...
The aim of this paper is to present a multiple object tracking data fusion technique, which fuses radar, image, and ego vehicle odometry. The data are fused at a high level, which leads to reliable and stable tracking results providing also additional features as width estimation and the detection of stationary objects. A ldquorealrdquo application of these algorithms is illustrated on a specific...
This contribution presents a fusion method for multivariate stereo and spectral series with the purpose of obtaining 3D information. The image series are gained using a camera array with spectral filters. In order to register them, features that are invariant with respect to the intensity values in the images are extracted. The fusion approach is region based and uses characteristics like their size,...
The objective of this research is to investigate and provide a proof-of-concept demonstration of how to approach the biosensor fusion process as a systems optimization. Recent work on biosensor fusion is disjointed and compartmentalize at each technical challenge of a very complex problem. Here we try to take a systems approach in deciding the following questions: Where to locate sensors? What sensors...
Many acoustic factors can contribute to the classification accuracy of ground vehicles. Classification based on Acoustic information fusion for ground vehicle classification a single feature set may lose some useful information. To obtain more complete knowledge regarding vehiclespsila acoustic characteristics, we propose a fusion approach to combine two sets of features, in which various aspects...
We propose an object classification system that incorporates information from a video camera and an automotive radar. The system implements three processes. The first process is attention selection, in which the radar guides a selection of a small number of candidate images for analysis by the camera and our learning method. In the second process, normalized attention windows are processed by orientation-selective...
In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel...
This paper considers the problem of the classification of objects observed by vehicle embedded sensors. We propose a general architecture and an algorithm to perform multisensor fusion for the classification purpose. The proposed solution has to be robust and flexible. The robustness is essential because this system is for safety applications. The flexibility is ensured by a modular architecture alongside...
In this paper, we present various approaches for combining classifiers to improve classification of textured images, which are not generally used in this application framework. This is what we call post-classification step of textured images. Three approaches to combine classifiers are presented: the majority voting approach, belief approach, and classification-based approach. Belief, majority voting...
This paper proposes a new method for multisensor background extraction and updating aimed at surveillance and target detection applications. The background scene extraction is based on robust multisensor change detection of moving objects in the scene. An iterative mechanism updates the background estimate using this information thereby ignoring transient objects but allowing for slow changes in scene...
In this paper, we propose a novel method for robustly classifying visual concepts. In order to achieve this aim, we propose a scheme that relies on Self Organizing Maps (SOM [6]). Heterogeneous local signatures are first extracted from training images and projected into specialized SOM networks. The extracted signatures activate several neural maps producing activation histograms. These activation...
In this paper, unsupervised clustering of normal vessel traffic patterns is proposed and implemented, where patterns are represented as the momentary location, speed and course of tracked vessels. The learnt cluster models are used for anomaly detection in sea traffic. The Gaussian Mixture Model is used as cluster model and a greedy version of the Expectation-Maximization algorithm is used as clustering...
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