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This paper presents an efficient real-time implementation of an unsupervised textile fabric defect detection algorithm called ITT using the concept of iterative tensor tracking on graphics processing unit (GPU). The algorithm adopts a new local image descriptor, Spatial Histograms of Oriented Gradients (S-HOG), which is shift-invariant, light insensitive and space scalable. For a given textile fabric...
In this paper we present a system for mobile augmented reality (AR) based on visual recognition. We split the tasks of recognizing an object and tracking it on the user's screen into a server-side and a client-side task, respectively. The capabilities of this hybrid client-server approach are demonstrated with a prototype application on the Android platform, which is able to augment both stationary...
We proposed a tracking and location method for UAVs' Vision System. In our method, the target is extracted from the ROI (Region of Interested) automatically by an analysis of intensity value distribution; then the mean-shift tracker is used to get the target's position in the sequence images; The relative position of the target to the vision system in the real world is reconstructed just by monocular...
This paper presents a completely automated 3D facial feature tracking system using 2D+3D image sequences recorded by a real-time 3D sensor. It is based on local feature detectors constrained by a 3D shape model, using techniques that make it robust under pose and partial occlusion. Several experiments conducted under relatively non-controlled conditions demonstrate the accuracy and robustness of the...
This paper presents a method for rectifying video sequences from rolling shutter (RS) cameras. In contrast to previous RS rectification attempts we model distortions as being caused by the 3D motion of the camera. The camera motion is parametrised as a continuous curve, with knots at the last row of each frame. Curve parameters are solved for using non-linear least squares over inter-frame correspondences...
We derive a probabilistic framework for robust, realtime, visual tracking of multiple previously unseen objects from a moving camera. This framework models the discrete depth ordering of the objects being tracked in the scene. The method uses the observed image data to compute a posterior over the objects' poses, shapes and relative depths. The poses are group transformations, the shapes are implicit...
Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force search to perform object tracking on the generated probability map, which is obtained from the classification results; however, performing probabilistic object tracking on the probability map is almost unexplored. This paper...
We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of patterns of activity rely on trajectories obtained from object detection and tracking systems, which are unreliable in complex scenes of crowded motion. We propose a mixture model representation of salient patterns of optical flow,...
We present an automatic and efficient method to extract spatio-temporal human volumes from video, which combines top-down model-based and bottom-up appearance-based approaches. From the top-down perspective, our algorithm applies shape priors probabilistically to candidate image regions obtained by pedestrian detection, and provides accurate estimates of the human body areas which serve as important...
We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking...
We present a method that unifies tracking and video content recognition with applications to Mobile Augmented Reality (MAR). We introduce the Radial Gradient Transform (RGT) and an approximate RGT, yielding the Rotation-Invariant, Fast Feature (RIFF) descriptor. We demonstrate that RIFF is fast enough for real-time tracking, while robust enough for large scale retrieval tasks. At 26× the speed, our...
In this paper, a general algorithm for pedestrian detection by on-board monocular camera which can be applied to cameras of various view ranges in unified manner. The Spatio-Temporal MRF model extracts and tracks foreground objects as pedestrians and non-pedestrian distinguishing from background scenes as buildings by referring to motion difference. During the tracking sequences, cascaded HOG classifiers...
Quantitative analysis of cardiac motion is important in assessing ventricular function. Multi-scale phase-based optical flow (OF) method for motion tracking of the left ventricle (LV) is presented for tagged magnetic resonance (MR) image sequence. Two phase images, which contain the information about the motion orthogonal to the tag lines, are derived from harmonic phase (HARP) technique for each...
Vehicle velocity estimation is an important aspect of intelligent transportation systems. Normally velocity is estimated using dedicated laser speed traps and Doppler radars. Recently, the use of cameras is becoming more common for the purpose of traffic surveillance and smart surveillance system. It is thus the aim of this paper to propose a method for vehicle speed estimation using these existing...
The accuracy of observation model to represent of natural object is critical to performing robust visual tracking in complex scenarios. In this paper, we propose an object observation model by considering three integrated feature components, i.e. the template component depicting the object structure in every time instant; the contour component characterizing the object shape evolution with local deformation,...
In the case a scenery consisting of multiple moving objects has to be observed and analyzed by using radar, it may occur that extended objects cause more than one observation. As a consequence, a conventional tracking algorithm, that bases on the assumption of point objects, has to process lots of observations, generates several tracks per object and thus is slowed down distinctly. Moreover, it is...
Remote patient monitoring can improve the quality of life of elderly and impaired people, while reducing the costs. Among the most interesting technologies being investigated, computer vision has proved to be very effective in several important scenarios in which conventional sensors fail or are impractical. We propose a computer vision-based wireless sensor system for people remote tracking and monitoring...
This paper introduces an embedded architecture and the low-level video processing algorithms developed for an intelligent node that is a part of a distributed intelligent sensory network for surveillance purposes. In this paper, details of the architecture developed for this node are given, together with the low-level video processing algorithms used, as well as the results obtained after their implementation...
Surveillance videos are often compressed for transmission or storage. It is desirable to be able to perform automatic event detection in the compressed domain directly. In this paper, we investigate the use of motion trajectories for video activity detection in the compressed domain. We show that it is possible to extract reliable motion trajectories directly from compressed H.264 video streams. To...
Object-class independent motion estimation from range data is a challenging task. We present here a novel approach that is able to derive a dense motion field based on range images only. We propose to first segment the range image into segments using a recently proposed segmentation criterion. Motion is then estimated segment-wise with full 6 degrees of freedom. To that end, we introduce dynamic mapping,...
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