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The automated tracking of social insects, such as ants, could dramatically increase the fidelity and amount of analyzed data for studying complex group behaviors. Recently, data association based multiple object tracking methods have shown promise in improving handling of occlusions. However, the tracking of ants in a colony is still challenging as (1) their motion is often sporadic and irregular...
Finding the outline of an object in an image is a fundamental step in many vision-based applications. It is important to demonstrate that the segmentation found accurately represents the contour of the object in the image. The discrepancy measure model for segmentation analysis focuses on selecting an appropriate discrepancy measure to compute a score that indicates how similar a query segmentation...
We propose a novel approach for multi-camera autocalibration by observing multiview surveillance video of pedestrians walking through the scene. Unlike existing methods, we do NOT require tracking or explicit correspondences of the same person across time/views. Instead, we take noisy foreground blobs as the only input and rely on a joint optimization framework with robust statistics to achieve accurate...
Live wide-area persistent surveillance (WAPS) systems must provide effective multi-target tracking on downlinked video streams in real-time. This paper presents the first published aerial tracking system that is documented to process over 100 megapixels per second. The implementation addresses the challenges with the mosaicked, low-resolution, grayscale NITF imagery provided by most currently fielded...
Secure Computation of Face Identification (SCiFI) [20] is a recently developed secure face recognition system that ensures the list of faces it can identify (e.g., a terrorist watch list) remains private. In this work, we study the consequences of malformed input attacks on the system — from both a security and computer vision standpoint. In particular, we present 1) a cryptographic attack that allows...
Minimally invasive procedures are of importance in modern surgery due to reduced operative trauma and recovery time. To enable robot assisted interventions, automatic tracking of endoscopie tools is an essential task. State-of-the-art techniques rely on 2-D color information only which is error prone for varying illumination and unpredictable color distribution within the human body. In this paper,...
Visual tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variations in illumination. In this paper, we propose a novel algorithm based on discrete cosine transform (DCT) to handle illumination variations, since illumination variations are mainly reflected in the low-frequency band. For instance, low illumination in a frame leads...
Over the years, a large number of methods have been proposed to analyze human pose and motion information from images, videos, and recently from depth data. Most methods, however, have been evaluated on datasets that were too specific to each application, limited to a particular modality, and more importantly, captured under unknown conditions. To address these issues, we introduce the Berkeley Multimodal...
Recognizing continuous action composition in human behavior is an important and yet challenging problem. In this paper we tackle the task by developing both reliable image features and classification algorithms. For image features, we introduce the Embedded Optical Flow (EOF) feature based on embedding optical flow using Locality-constrained Linear Coding with weighted average pooling. The EOF feature...
This paper presents a framework for N-view triangulation of scene points, which improves processing time and final reprojection error with respect to standard methods, such as linear triangulation. The framework introduces an angular error-based cost function, which is robust to outliers and inexpensive to compute, and designed such that simple adaptive gradient descent can be applied for convergence...
This paper addresses the problem of interactive image segmentation. We propose an extension of the GrowCut framework which follows Cellular Automaton theory and is comparable to a label propagation algorithm. Therefore, user labels are propagated according to Cellular Automaton until convergency. A common problem of GrowCut is the time consuming user initialization which requires distributed seeds...
Human identification based on iris biometrics requires high resolution iris images of a cooperative subject. Such images cannot be obtained in non-intrusive applications such as surveillance. However, the full region around the eye, known as the periocular region, can be acquired non-intrusively and used as a biometric. In this paper we investigate the use of periocular region for person identification...
In this paper we propose a new data fitting method which, similar to RANSAC, fits data to a model using sample and consensus. The application of interest is fitting 3D point clouds to a prior geometric model. Where the RANSAC process uses random samples of points in the fitting trials, we propose a novel method which directs the sampling by ordering the points according to their contribution to the...
A recent trend in computer vision is to represent images through covariance matrices, which can be treated as points on a special class of Riemannian manifolds. A popular way of analysing such manifolds is to embed them in Euclidean spaces, a process which can be interpreted as warping the feature space. Embedding manifolds is not without problems, as the manifold structure may not be accurately preserved...
We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions...
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