Browsing by Subject "Target tracking"
Now showing 1 - 13 of 13
Results Per Page
Sort Options
Item Open Access Çizge kesit yöntemi ile hiperspektral görüntülerde anomali tabanlı hedef tespiti(IEEE, 2015-05) Batı, E.; Erdinç, Acar; Çeşmeci, D.; Çalışkan, A.; Koz, A.; Aksoy, Selim; Ertürk, S.; Alatan, A. A.Hiperspektral hedef tespiti için yürütülen çalışmalar genel olarak iki sınıfta degerlendirilebilir. İlk sınıf olan anomali tespit yöntemlerinde, hedefin görüntünün geri kalanından farklı oldugu bilgisi kullanılarak görüntü analiz edilmektedir. Diğer sınıfta ise daha önceden bilgisi edinilmiş hedefe ait spektral imza ile görüntüdeki herbir piksel arasındaki benzerlik bulunarak hedefin konumu tespit edimektedir. Her iki sınıf yöntemin de önemli bir dezavantajı hiperspektral görüntü piksellerini bagımsız olarak degerlendirip, aralarındaki komşuluk ilişkilerini gözardı etmesidir. Bu makalede anomali tespit ve imza tabanlı tespit yakla¸sımlarını, pikseller arası komşuluk ilişkilerini de göz önünde bulundurarak birleştiren çizge yaklaşımına dayalı yeni bir yöntem önerilmiştir. Hedeflerin hem imza bilgisine sahip olundugu hem de anomali sayılabilecek ölçülerde olduğu varsayılarak önerilen çizge yaklaşımında önplan için imza bilgisi kullanan özgün bir türev tabanlı uyumlu filtre önerilmiştir. Arkaplan için ise seyreklik bilgisi kullanarak Gauss karışım bileşeni kestirimi yapan yeni bir anomali tespit yöntemi geliştirilmiştir. Son olarak komşular arası benzerligi tanımlamak için ise spektral bir benzerlik ölçütü olan spektral açı eleştiricisi kullanılmıştır. Önerilen çizge tabanlı yöntemin önplan, arkaplan ve komşuluk ilişkilerini uygun şekilde birleştirdigi ve önceki yöntemlere göre hedefi gürültüden arınmış bir bütün şeklinde başarıyla tespit edebildigi gözlemlenmiştir. The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat each pixel of the hyperspectral image individually, without considering the neighbourhood relations between the pixels. In this paper, we propose a target detection algorithm which combines the anomaly detection and signature based hyperspectral target detection approaches in a graph based framework by utilizing the neighbourhood relations between the pixels. Assuming that the target signature is available and the target sizes are in the range of anomaly sizes, a novel derivative based matched filter is first proposed to model the foreground. Second, a new anomaly detection method which models the background as a Gaussian mixture is developed. The developed model estimates the optimal number of components forming the Gaussian mixture by means of utilizing sparsity information. Finally, the similarity of the neighbouring hyperspectral pixels is measured with the spectral angle mapper. The overall proposed graph based method has successfully combined the foreground, background and neighbouring information and improved the detection performance by locating the target as a whole object free from noises. © 2015 IEEE.Item Open Access Comparative analysis of different approaches to target differentiation and localization with sonar(Elsevier, 2003) Barshan, B.; Ayrulu, B.This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-2ight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained usingpre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.Item Open Access H ∞-filter based target tracking under time delayed measurements(IEEE, 2012) Ateş, Ezgi; Özbay, HitayIn this paper a new filter structure is proposed for the H ∞ estimation under delayed measurements for continuous time processes. As an example, target tracking problem is considered and results obtained from the classical H 2-optimal and the proposed H ∞-optimal filters are compared. © 2012 IEEE.Item Open Access H∞ filter design for vehicle tracking under delayed and noisy measurements(IEEE, 2007-06) Ezercan, Sami; Özbay, HitayIn many intelligent vehicles applications tracking plays an important role. This paper considers tracking of a vehicle under delayed and noisy measurements. For this purpose we design an H∞ optimal filter for linear systems with time delays in the state and output variables. By using the duality between filtering and control, the problem at hand is transformed to a robust controller design for systems with time delays. The skew Toeplitz method developed earlier for the robust control of infinite dimensional systems is used to solve the H∞ filtering problem. The results are illustrated with simulations and effects of the time delay on the tracking performance are demonstrated. ©2007 IEEE.Item Open Access Incorporating doppler velocity measurement for track initiation and maintenance(IET, 2006) Kural, F.; Arıkan, F.; Arıkan, Orhan; Efe, M.Performance of multiple target tracking algorithms in complex environments heavily relies on the success of track initiation and measurement-to-track association algorithms. Doppler velocity measurement is the major discriminant of clutter from the target of interest with relatively higher velocities. This work summarizes the analytical derivations and presents simulation results about track initiation and maintenance using Doppler velocity reports along with the 3D position measurements extracted by a phased array radar.Item Open Access İstatistiksel örüntü tanıma teknikleri kullanarak kızılberisi algılayıcılarla hedef ayırdetme(IEEE, 2006-04) Aytaç, Tayfun; Yüzbaşıoǧlu, Çağrı; Barshan, BillurThis study compares the performances of different statistical pattern recognition techniques to differentiation of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders, using low-cost infrared sensors. The pattern recognition techniques compared include parametric density estimation, mixture of Gaussians, kernel estimator, k-nearest neighbor classifier, neural network classifier, and support vector machine classifier. A correct differentiation rate of 100% is achieved for six surfaces using parametric differentiation. For three geometries covered with seven different surfaces, best correct differentiation rate (100%) is achieved with mixture of Gaussians classifier with three components. The results demonstrate that simple infrared sensors, when coupled with appropriate processing, can be used to extract substantially more information than such devices are commonly employed. © 2006 IEEE.Item Open Access Manoeuvring-target tracking with the Viterbi algorithm in the presence of interference(IET, 1989) Demirbaş, K.The Viterbi algorithm is used to track a target in the presence of random interference, such as jamming. A nonlinear target motion and an observation which is modelled in a spherical coordinate system are considered. The observation model is a nonlinear function of interference. The components of the state vector are the range, bearing angle, and elevation angle of the target location. The state vector is estimated, component-by-component, by a parallel use of the Viterbi algorithm in blocks. Simulation results, some of which are presented, have shown that the proposed estimation scheme performs well, whereas classical estimation schemes, such as the extended Kalman filter, cannot, in general, handle target tracking in the presence of random interference.Item Open Access Multiperson tracking with a network of ultrawideband radar sensors based on gaussian mixture PHD filters(Institute of Electrical and Electronics Engineers Inc., 2015) Gulmezoglu, B.; Guldogan, M. B.; Gezici, SinanIn this paper, we investigate the use of Gaussian mixture probability hypothesis density filters for multiple person tracking using ultrawideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a computer is designed, and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach. © 2014 IEEE.Item Open Access Object tracking under illumination variations using 2D-cepstrum characteristics of the target(IEEE, 2010) Cogun, Fuat; Çetin, A. EnisMost video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In this paper, a 2D-cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target object provides robustness to varying illumination conditions. Another contribution of the paper is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method. ©2010 IEEE.Item Open Access Performance evaluation of track association and maintenance for a MFPAR with doppler velocity measurements(2010) Kural, F.; Arikan, F.; Arıkan, Orhan; Efe, M.This study investigates the effects of incorporating Doppler velocity measurements directly into track association and maintenance parts for single and multiple target tracking unit in a multi function phased array radar (MFPAR). Since Doppler velocity is the major discriminant of clutter from a desired target, the measurement set has been expanded from range, azimuth and elevation angles to include Doppler velocity measurements. We have developed data association and maintenance part of a well known tracking method, Interacting Multiple Model Probabilistic Data Association.Item Open Access Region covariance descriptors calculated over the salient points for target tracking(IEEE, 2012) Çakir, S.; Aytaç, T.; Yildirim, A.; Beheshti, S.; Gerek Ö.N.; Çetin, A. EnisFeatures extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasi's minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure. © 2012 IEEE.Item Open Access Statistical pattern recognition techniques for target differentiation using infrared sensor(IEEE, 2006) Aytaç, Tayfun; Yüzbaşıoğlu, Ç.; Barshan, BillurThis study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for.Item Open Access Target detection and classification in SAR images using region covariance and co-difference(SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. EnisIn this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.