Recent research in motion detection has shown that various outlier detection methods could be used for efficient
detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute
space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the
performance of two incremental outlier detection algorithms, namely the incremental connectivity-based outlier factor
and the incremental local outlier factor to modified Stauffer-Grimson algorithm. Each video sequence is represented
with spatial-temporal blocks extracted from the raw video. Principal component analysis (PCA) is applied on these
blocks in order to reduce the dimensionality of extracted data. Extensive experiments performed on several data sets,
including infrared sequences from OSU Thermal Pedestrian Database repository, and data collected at Delaware State
University from FLIR Systems PTZ cameras have shown promising results in using outlier detection for detection of
small moving targets.
Proc. SPIE. 7445, Signal and Data Processing of Small Targets 2009
KEYWORDS: Detection and tracking algorithms, Databases, Video, Scalable video coding, Data modeling, Improvised explosive devices, Expectation maximization algorithms, Video surveillance, Data mining, Image analysis
Detection of unusual trajectories of moving objects can help in identifying suspicious activity on convoy routes and thus
reduce casualties caused by improvised explosive devices. In this paper, using video imagery we compare efficiency of
various techniques for incremental outlier detection on detecting unusual trajectories on simulated and real-life data
obtained from SENSIAC database. Incremental outlier detection algorithms that we consider in this paper include
incremental Support Vector Classifier (incSVC), incremental Local Outlier Factor (incLOF) algorithm and incremental
Connectivity Outlier Factor (incCOF) algorithm. Our experiments performed on ground truth trajectory data indicate that
incremental LOF algorithm can provide better detection of unusual trajectories in comparison to other examined
This paper provides an overview of our efforts in detecting cyber attacks in networked information systems. Traditional signature based techniques for detecting cyber attacks can only detect previously known intrusions and are useless against novel attacks and emerging threats. Our current research at the University of Minnesota is focused on developing data mining techniques to automatically detect attacks against computer networks and systems. This research is being
conducted as a part of MINDS (Minnesota Intrusion Detection System) project at the University of Minnesota. Experimental results on live network traffic at the University of Minnesota show that the new techniques show great promise in detecting novel intrusions. In particular, during the past few months our techniques have been successful in automatically identifying several novel intrusions that could not be detected using state-of-the-art tools such as SNORT.
In this paper, a novel data mining approach to address damage detection within the large-scale complex structures is proposed. Every structure is defined by the set of finite elements that also represent the number of target variables. Since large-scale complex structures may have extremely large number of elements, predicting the failure in every single element using the original set of natural frequencies as features is exceptionally time-consuming task. Therefore, in order to reduce the time complexity we propose a hierarchical localized approach for partitioning the entire structure into substructures and predicting the failure within these substructures. Unlike our previous sub-structuring approach, which is based on physical substructures in the structure, here we propose to partition the structure into sub-structures employing hierarchical clustering algorithm that also allows localizing the damage in the structure. Finally, when the identified substructure with a failure consists of sufficiently small number of target variables the extent of the damage in the element of the substructure is predicted. A numerical example analyses on an electric transmission tower frame is presented to demonstrate the effectiveness of the proposed method.
We propose partitioning-based methods to facilitate the classification of 3-D binary image data sets of regions of interest (ROIs) with highly non-uniform distributions. The first method is based on recursive dynamic partitioning of a 3-D volume into a number of 3-D hyper-rectangles. For each hyper-rectangle, we consider, as a potential attribute, the number of voxels (volume elements) that belong to ROIs. A hyper-rectangle is partitioned only if the corresponding attribute does not have high discriminative power, determined by statistical tests, but it is still sufficiently large for further splitting. The final discriminative hyper-rectangles form new attributes that are further employed in neural network classification models. The second method is based on maximum likelihood employing non-spatial (k-means) and spatial DBSCAN clustering algorithms to estimate the parameters of the underlying distributions. The proposed methods were experimentally evaluated on mixtures of Gaussian distributions, on realistic lesion-deficit data generated by a simulator conforming to a clinical study, and on synthetic fractal data. Both proposed methods have provided good classification on Gaussian mixtures and on realistic data. However, the experimental results on fractal data indicated that the clustering-based methods were only slightly better than random guess, while the recursive partitioning provided significantly better classification accuracy.