Browsing by Subject "Object Tracking"

Now showing 1 - 7 of 7

Open Access
Automatic detection of salient objects for a video database system
(2005) Sevilmiş, Tarkan
Recently, the increase in the amount of multimedia data has unleashed the development of storage techniques. Multimedia databases is one of the most popular of these techniques because of its scalability and ability to be queried by the media features. One downside of these databases is the necessity for processing of the media for feature extraction prior to storage and querying. Ever growing pile of media makes this processing harder to be completed manually. This is the case with BilVideo Video Database System, as well. Improvements on computer vision techniques for object detection and tracking have made automation of this tedious manual task possible. In this thesis, we propose a tool for the automatic detection of objects of interest and deriving spatio-temporal relations between them in video frames. The proposed framework covers the scalable architecture for video processing and the stages for cut detection, object detection and tracking. We use color histograms for cut detection. Based on detected shots, the system detects salient objects in the scene, by making use of color regions and camera focus estimation. Then, the detected objects are tracked based on their location, shape and estimated speed.
Open Access
Çarpıcıdan bağımsız ortak fark matrisi kullanarak video ve görüntü işleme
(IEEE, 2009-04) Çetin, A. Enis; Duman, Kaan; Tuna, Hakan; Eryıldırım, Abdulkadir
Bu bildiride gerçel sayılar üzerinde yarı grup kuran yeni bir iletmen tanımlayarak elde edilen bir bölge betimleyicisi ile hareketli obje takibi, yüz sezimi, plaka bulma, bölge betimleme için kullanılabilecek hızlı bir algoritma sunuyoruz. Bu yeni iletmen hiçbir çarpma gerektirmez. Bu iletmeni kullanarak, imge bölgelerini nitelendiren ve ortak fark adı verilen bir matris tanımlıyoruz. Plaka bulma uygulamasında ortak fark matrislerinı plaka bölgelerinden kestirip, bunları bir veritabanında saklıyoruz. Plaka bölgelerini gerçek zamanlı videoda tanımlamak için ilk önce videodaki hareketli bölgeleri taşıyan imgeleri belirliyoruz, sonra hareketli bölgelerin içinde ya da bütün resim içinde plaka büyüklüğündeki bölgelerin ortak ayrık matrislerini veritabanındaki plaka ortak ayrık matrisleriyle karşılaştırarak bölge içinde plaka olup olmadığını belirliyoruz.
Open Access
Cepstrum based method for moving shadow detection in video
(Springer, 2010-09) Cogun, Fuat; Çetin, A. Enis
Moving shadows constitute problems in various applications such as image segmentation and object tracking. Main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, the cepstrum based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made. © 2011 Springer Science+Business Media B.V.
Open Access
Moving object detection, tracking and classification for smart video surveillance
(2004) Dedeoğlu, Yiğithan
Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of “smart” video surveillance systems has become a critical requirement. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In this thesis, a smart visual surveillance system with real-time moving object detection, classification and tracking capabilities is presented. The system operates on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The classification algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group and vehicle. The system is also able to detect the natural phenomenon fire in various scenes reliably. The proposed tracking algorithm successfully tracks video objects even in full occlusion cases. In addition to these, some important needs of a robust smart video surveillance system such as removing shadows, detecting sudden illumination changes and distinguishing left/removed objects are met.
Open Access
Moving shadow detection in video using cepstrum
(SAGE, 2013) Cogun, F.; Çetin, A. Enis
Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum-based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made. © 2013 Cogun and Cetin; licensee InTech.
Open Access
Object tracking under illumination variations using 2D-cepstrum characteristics of the target
(IEEE, 2010) Cogun, Fuat; Çetin, A. Enis
Most 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.
Open Access
Visual object tracking using co-difference features
(2017-08) Demir, Hüseyin Seçkin
Visual object tracking has been one of the widely studied computer vision tasks which has a broad range of applications in various areas from surveillance to medical studies. There are different approaches proposed for the problem in the literature. While some of them use generative methods where an appearance model is built and used for localizing the object on the image, others use discriminative approaches that models the object and background as two different classes and turns the tracking task into a binary classification problem. In this study, we propose a novel object tracking algorithm based on co-difference matrix and compare its performance with the recent state-of-the-art tracking algorithms on two specific applications. Experiments on a large class of datasets show that the proposed co-difference based object tracking algorithm has successful results in terms of track maintenance, success rate and localization accuracy. The proposed algorithm uses co-difference matrix as the image descriptor. Extraction of co-difference features is similar to the well known covariance method. However the vector product operator is redefined in a multiplication-free manner. The new operator yields a computationally efficient implementation for real time object tracking applications. For our experiments, we prepared a comparison framework that contains over 70000 annotated images for visual object tracking task. We conducted experiments for two different application areas seperately. The first one is infrared surveillance sytems. For this application, we used a thermal image dataset that contains various objects such as humans, cars and military vehicles. The second application area is cell tracking on time-lapse microscopy images. Image sequences for the second application contain cells of different shapes and sizes. For both applications, datasets include a considerable amount of rotation and background clutter. Performance of the tracking algorithms are evaluated quantitatively based on three different metrics. These metrics measure the track maintenance score, success rate and localization accuracy of an algorithm. Experiments indicate that the proposed co-difference based tracking algorithm is among the best performing methods by having the highest localization accuracy and success rate for the surveillance dataset, and the highest track maintenance score for the cell motility dataset.