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Incorporating 3D models for face hallucination (FH) is an ill-posed problem in light of the low-resolution (LR) conditions. To deal with the challenges, a specific 3D shape modeling approach targeting LR face images is first proposed. Based on a few automatically detected 2D facial feature points, an adaptive fitting scheme to relax the fixed correspondence assumption on the facial contour is devised,...
Incorporating 3D information has proven to be effective in many computer vision tasks and it is no exception in the context of facial analysis. However, limited application has been witnessed in face hallucination (FH), probably due to the difficulty of fitting 3D models onto low-resolution (LR) images. This paper presents a pure 3D approach to address this problem. By extending the LR image formation...
In this paper, we propose a new characteristic measure relative people density and motion dynamics for the purpose of long-term crowd monitoring. While many related works focus on direct people counting and absolute density estimation, we will show that relative densities provide reliable information on crowd behaviour. Furthermore, we will discuss the derivation of a so-called Congestion Level of...
Super-resolution (SR) offers an effective approach to boost quality and details of low-resolution (LR) images to obtain high-resolution (HR) images. Despite the theoretical and technical advances in the past decades, it still lacks plausible methodology to evaluate and compare different SR algorithms. The main cause to this problem lies in the missing ground truth data for SR. Unlike in many other...
This paper presents a concept which tackles the pose estimation problem (extrinsic calibration) for distributed, non-overlapping multi-camera networks. The basic idea is to use a visual SLAM technique in order to reconstruct the scene from a video which includes areas visible by each camera of the network. The reconstruction consists of a sparse, but highly accurate point cloud, representing a joint...
In this paper, contactless palm and finger detection for biometric fingerprint verification/identification process with mobile devices is considered. In order to speed up the border checking verification process, we focus on capturing the whole palm in order to extract each fingertip instead of successively capturing each fingertip. The workflow comprises palm detection in order to detect the skin...
Touchless finger detection for the biometric fingerprint verification/identification process with mobile devices is considered in this paper. Fingerprint capturing is based on a camera system with bright-field illumination. For finger detection, a machine learning based algorithm with Aggregated Channel Features (ACFs) and a skin-color based finger segmentation with a geometric shape based approach...
Most state-of-the-art solutions for localizing facial feature landmarks build on the recent success of the cascaded regression framework [7, 15, 34], which progressively predicts the shape update based on the previous shape estimate and its feature calculation.
Face Hallucination (FH) differs from generic single-image super-resolution (SR) algorithms in its specific domain of application. By exploiting the common structures of human faces, magnification of lower resolution images can be achieved. Despite the growing interest in recent years, considerably less attention is paid to a crucial step in FH -- registration of facial images. In this work, registration...
This paper presents a fully automatic system that recovers 3D face models from sequences of facial images. Unlike most 3D Morphable Model (3DMM) fitting algorithms that simultaneously reconstruct the shape and texture from a single input image, our approach builds on a more efficient least squares method to directly estimate the 3D shape from sparse 2D landmarks, which are localized by face alignment...
In this work we present a new dataset for the tasks person detection, tracking, re-identification, and soft-biometric attribute detection in surveillance data. The dataset was recorded over three days and consists of more than 30 individuals moving through a network of seven cameras. Person tracks are labeled with consistent IDs as well as soft-biometric attributes, such as a description of the clothing,...
Deformable model fitting to high-resolution facial images has been extensively studied for over two decades. However, due to the ill-posed problem caused by low-resolution images, most existing work cannot be applied directly and degrades quickly as the resolution decreases. To address this issue, this paper extends the Constrained Local Model (CLM) to a multi-resolution model consisting of a 4-level...
Robust people re-identification is one of the most challenging task, and still an unsolved problem for several applications in video surveillance. A large number of approaches use colors as main features for object description, which are in fact important cues for re-identification. However, colors captured by a camera suffer from unknown and changing global and local illumination conditions in the...
The CamInSens system is a next-generation self-organizing video surveillance system that combines research being done in the fields of person-tracking, trajectory analysis, visual analytics, and self-organizing system management algorithms. Its purpose is the online threat detection by analysing anomalies in persons' trajectories. Therefore, robust multi-camera multi-person tracking is combined with...
While for static cameras several background subtraction approaches have been developed in the past, for non-static pan/tilt cameras efficient and robust motion detection is still a challenging task. Known approaches use image-to-image registration methods to generate a panorama background model of the scene, which spans a joint pixel coordinate system for later background estimation and subtraction...
In this paper a task-oriented approach for object tracking in large distributed camera networks is presented. This work includes three main contributions. First a generic process framework is presented, which has been designed for task-oriented video processing. Second, system components of the task-oriented framework needed for the task of multi-camera person tracking are introduced in detail. Third,...
In this paper an approach for dynamic camera selection in large video-based sensor networks for the purpose of multi-camera object tracking is presented. The sensor selection approach is based on computational geometry algorithms and is able to determine task-relevant cameras (camera cluster) by evaluation of geometrical attributes, given the last observed object position, the sensor configurations...
Detection of moving objects is a fundamental task in video based surveillance and security applications. Many detection systems use background estimation methods to model the observed environment. In outdoor surveillance, moving backgrounds (waving trees, clutter) and illumination changes (weather changes, reflections, etc.) are the major challenges for background modelling and the development of...
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