Olga Zoidi

Visual Object Tracking Based on Local Steering Kernels and Color Histograms

In this paper, we propose a visual object tracking framework, which employs an appearance-based representation of the target object, based on local steering kernel descriptors and color histogram information. This framework takes as input the region of the target object in the previous video frame and a stored instance of the target object, and tries to localize the object in the current frame by finding the frame region that best resembles the input. As the object view changes over time, the object model is updated, hence incorporating these changes. Color histogram similarity between the detected object and the surrounding background is employed for background subtraction. Experiments are conducted to test the performance of the proposed framework under various conditions. The proposed tracking scheme is proven to be successful in tracking objects under scale and rotation variations and partial occlusion, as well as in tracking rather slowly deformable articulated objects.

Person Identity Label Propagation in Stereo Videos

In this paper a novel method is introduced for propagating person identity labels on facial images extracted from stereo videos. It operates on image data with multiple representations and calculates a projection matrix that preserves locality information and a priori pairwise information, in the form of must-link and cannot-link constraints between the various data representations. The final data representation is a linear combination of the projections of all data representations. Moreover, the proposed method takes into account information obtained through data clustering. This information is exploited during the data propagation step in two ways: to regulate the similarity strength between the projected data and to indicate which samples should be selected for label propagation initialization. The performance of the proposed Multiple Locality Preserving Projections with Cluster-based Label Propagation (MLPP-CLP) method was evaluated on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods.

Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification

The state-of-the-art classification methods which employ nonnegative matrix factorization (NMF) employ two consecutive independent steps. The first one performs data transformation (dimensionality reduction) and the second one classifies the transformed data using classification methods, such as nearest neighbor/centroid or support vector machines (SVMs). In the following, we focus on using NMF factorization followed by SVM classification. Typically, the parameters of these two steps, e.g., the NMF bases/coefficients and the support vectors, are optimized independently, thus leading to suboptimal classification performance. In this paper, we merge these two steps into one by incorporating maximum margin classification constraints into the standard NMF optimization. The notion behind the proposed framework is to perform NMF, while ensuring that the margin between the projected data of the two classes is maximal. The concurrent NMF factorization and support vector optimization are performed through a set of multiplicative update rules. In the same context, the maximum margin classification constraints are imposed on the NMF problem with additional discriminant constraints and respective multiplicative update rules are extracted. The impact of the maximum margin classification constraints on the NMF factorization problem is addressed in Section VI. Experimental results in several databases indicate that the incorporation of the maximum margin classification constraints into the NMF and discriminant NMF objective functions improves the accuracy of the classification.

Stereo Object Tracking with Fusion of Texture, Color and Disparity Information

Abstract A novel method for visual object tracking in stereo videos is proposed, which fuses an appearance based representation of the object based on Local Steering Kernel features and 2D color–disparity histogram information. The algorithm employs Kalman filtering for object position prediction and a sampling technique for selecting the candidate object regions of interest in the left and right channels. Disparity information is exploited, for matching corresponding regions in the left and right video frames. As tracking evolves, any significant changes in object appearance due to scale, rotation, or deformation are identified and embodied in the object model. The object appearance changes are identified simultaneously in the left and right channel video frames, ensuring correct 3D representation of the resulting bounding box in a 3D display monitor. The proposed framework performs stereo object tracking and it is suitable for application in 3D movies, 3D TV content and 3D video content captured by consuming stereo cameras. Experimental results proved the effectiveness of the proposed method in tracking objects under geometrical transformations, zooming and partial occlusion, as well as in tracking slowly deforming articulated 3D objects in stereo video.

Appearance based object tracking in stereo sequences

A novel algorithm is proposed, that performs tracking of rigid objects in 3D videos, without knowledge of the camera calibration parameters, by exploiting only visual information obtained from the left and right video channels, namely luminance and disparity information. The proposed algorithm exploits noisy disparity maps that have been extracted by a real-time disparity estimation algorithm. The algorithm employs two appearance-based representation methods for describing the object texture. The first one combines luminance with disparity information and the second one employs Local Steering Kernel (LSK) descriptors.

An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing

The latest developments in 3D capturing, processing, and rendering provide means to unlock novel 3D application pathways. The main elements of an integrated platform, which target tele-immersion and future 3D applications, are described in this paper, addressing the tasks of real-time capturing, robust 3D human shape/appearance reconstruction, and skeleton-based motion tracking. More specifically, initially, the details of a multiple RGB-depth (RGB-D) capturing system are given, along with a novel sensors’ calibration method. A robust, fast reconstruction method from multiple RGB-D streams is then proposed, based on an enhanced variation of the volumetric Fourier transform-based method, parallelized on the Graphics Processing Unit, and accompanied with an appropriate texture-mapping algorithm. On top of that, given the lack of relevant objective evaluation methods, a novel framework is proposed for the quantitative evaluation of real-time 3D reconstruction systems. Finally, a generic, multiple depth stream-based method for accurate real-time human skeleton tracking is proposed. Detailed experimental results with multi-Kinect2 data sets verify the validity of our arguments and the effectiveness of the proposed system and methodologies.

Graph-Based Label Propagation in Digital Media: A Review

The expansion of the Internet over the last decade and the proliferation of online social communities, such as Facebook, Google+, and Twitter, as well as multimedia sharing sites, such as YouTube, Flickr, and Picasa, has led to a vast increase of available information to the user. In the case of multimedia data, such as images and videos, fast querying and processing of the available information requires the annotation of the multimedia data with semantic descriptors, that is, labels. However, only a small proportion of the available data are labeled. The rest should undergo an annotation-labeling process. The necessity for the creation of automatic annotation algorithms gave birth to label propagation and semi-supervised learning. In this study, basic concepts in graph-based label propagation methods are discussed. Methods for proper graph construction based on the structure of the available data and label inference methods for spreading label information from a few labeled data to a larger set of unlabeled data are reviewed. Applications of label propagation algorithms in digital media, as well as evaluation metrics for measuring their performance, are presented.

Prescribed performance control for robot joint trajectory tracking under parametric and model uncertainties

This work proposes a control law for the robot joint trajectory tracking in free space that achieves a prescribed performance of the joint position error under parametric uncertainties; the control law is extended for the case of bounded disturbances. A performance function incorporating predefined performance indices is used to produce a transformed error that is injected in the controller. Furthermore, asymptotic stability of the velocity error in case of zero disturbances and uniformly ultimate boundedness in an arbitrarily small region for bounded disturbances is achieved. Simulation results confirm the theoretical findings and compare the proposed controller with a conventional one.

Visual object tracking based on the object's salient features with application in automatic nutrition assistance

A novel method for object tracking in videos which can find application in eating and drinking activity recognition is proposed. The query object is detected in the first video frame, extracting a new query image. The initial query image along with the obtained query image are then compared with patches within a determined search region around the position of the detected object in the previous frame. For each image, the local steering kernels are extracted and the similarity between a query image and the patches of the video frame is measured by calculating the cosine similarity. The proposed method finds application in eating and drinking activity recognition.

Prescribed performance regulation for robot manipulators

Abstract A new controller for the joint position regulation of robot manipulators is proposed that achieves prescribed performance indices regarding the response of the joint position error. The control input incorporates a transformed error that ensures prescribed error performance which is not affected by constant disturbances and control gains. Control parameter selection is significantly simplified and is merely confined in achieving admissible input torques. A simulation example of a two degree of freedom robot is used to illustrate the theoretical results and compare the proposed controller with conventional control schemes.