Comparison of Infrared and Visible Imagery for Object Tracking: Toward Trackers with Superior IR Performance

The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery

Hareketli nesnelerin görsel tespiti ve izlenmesi.

In this study, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Background subtraction has been performed to detect the moving objects in the video, which has been taken from a static camera. Four methods, frame differencing, running (moving) average, eigenbackground subtraction and mixture of Gaussians, have been used in the background subtraction process. After background subtraction, using some additional operations, such as morphological operations and connected component analysis, the objects to be tracked have been acquired. While tracking the moving objects, active contour models (snakes) has been used as one of the approaches. In addition to this method; Kalman tracker and mean-shift tracker are other approaches which have been utilized. A new approach has been proposed for the problem of tracking multiple targets. We have implemented this method for single and multiple camera configurations. Multiple cameras have been used to augment the measurements. Homography matrix has been calculated to find the correspondence between cameras. Then, measurements and tracks have been associated by the new tracking method.M.S. - Master of Scienc

Riemann manifoldları üzerinde zaman serileri.

In this thesis, feature covariance matrices are utilized to solve several problems related to time series. In the first part of the thesis, a novel representation is proposed to represent the time series using feature covariance matrices. By this representation, time series are carried onto Riemannian manifold space. The proposed representation is firstly applied to trajectories which are essentially 2D time series. Anomaly detection and activity perception problems in crowded visual scenes are studied by using the trajectories. The second utilization of the proposed representation is for classification of 1D time series. The feature covariance matrices of overlapping subsequences are extracted and fed into two well-known classifiers as the input. The last contribution of the thesis is a rank-based distance measure for high dimensional covariance matrices. The distance measure is utilized to solve skeletal action recognition problem. Unlike classical distance measures, the rank-based distance measure enables us to learn the manifold structure. For this reason, essentially, it can be asserted that the proposed approach is about manifold learning. Performances of the approaches proposed in this thesis have been compared to most of the state-of-the-art techniques on publicly available well-known datasets. For all of the studied problems, we achieve comparable or outperforming results compared to the state-of-the-art techniques.Ph.D. - Doctoral Progra

Visual Tracking of Objects via Rule-based Multiple Hypothesis Tracking

In this paper, one of the most crucial step of a visual surveillance system is presented. To track the multiple objects in the scene, multiple hypothesis tracking is combined with the fuzzy logic. Mixture of Gaussians method has been used to detect the moving objects in the video, which is taken from a static camera. Kalman filter has been utilized to estimate the next state of the objects. After the estimation, current measurements have been compared with the estimated features by utilizing fuzzy rules. The proposed method has been tested for both single and multiple camera configurations

Anomaly Detection and Activity Perception Using Covariance Descriptor for Trajectories

In this work, we study the problems of anomaly detection and activity perception through the trajectories of objects in crowded scenes. For this purpose, we propose a novel representation for trajectories via covariance features. Representing trajectories via feature covariance matrices enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances between trajectories, anomaly detection is achieved by sparse representations on nearest neighbors and activity perception is achieved by extracting the dominant motion patterns in the scene through the use of spectral clustering. Conducted experiments show that the proposed method yields results which are outperforming or comparable with state of the art

Anomaly Detection in Trajectories

In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via covariance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances, anomaly detection is achieved by sparse representations on nearest neighbours. Conducted experiments on both synthetic and real datasets show that the proposed method yields results which are outperforming or comparable with state of the art

Time series classification with feature covariance matrices

In this work, a novel approach utilizing feature covariance matrices is proposed for time series classification. In order to adapt the feature covariance matrices into time series classification problem, a feature vector is defined for each point in a time series. The feature vector comprises local and global information such as value, derivative, rank, deviation from the mean, the time index of the point and cumulative sum up to the point. Extracted feature vectors for the time instances are concatenated to construct feature matrices for the overlapping subsequences. Covariances of the feature matrices are used to describe the subsequences. Our main purpose in this work is to introduce and evaluate the feature covariance representation for time series classification. Therefore, in classification stage, firstly, 1-NN classifier is utilized. After showing the effectiveness of the representation with 1-NN classifier, the experiments are repeated with SVM classifier. The other novelty in this work is that a novel distance measure is introduced for time series by feature covariance matrix representation. Conducted experiments on UCR time series datasets show that the proposed method mostly outperforms the well-known methods such as DTW, shapelet transform and other state-of-the-art techniques

Time Series Classification Using Point-wise Features

In this work, a novel approach utilizing feature covariance matrices is proposed for time series classification. In order to adapt the feature covariance matrices for time series classification, a feature vector is defined for each point in a time series. The feature vector comprises local and global information such as value, derivative, rank, deviation from the mean, time index of the point and cumulative sum up to the point. Instead of representing the whole time series with a single covariance matrix, time series is divided into overlapping subsequences. Extracted feature vectors for the time instances are concatenated to construct feature matrices for the overlapping subsequences. Covariance of the feature matrices are used to describe the subsequences. After the determination of feature covariance matrices for both training and test samples, SVM classifier is utilized to decide the class of the test samples. Conducted experiments on UCR time series dataset show that the proposed method yields results which mostly outperform well-known methods such as DTW, shapelets and other state-of-the-art techniques

Visual detection and tracking of moving objects

In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted foregrounds. Snakes have been also used as an extra tool for object tracking