Video Prioritization for Unequal Error Protection

We analyze the effect of packet losses in video sequences and propose a lightweight Unequal Error Protection strategy which, by choosing which packet is discarded, reduces strongly the Mean Square Error of the received sequenc

Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space

This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects

Real-time shot detection based on motion analysis and multiple low-level techniques

To index, search, browse and retrieve relevant material, indexes describing the video content are required. Here, a new and fast strategy which allows detecting abrupt and gradual transitions is proposed. A pixel-based analysis is applied to detect abrupt transitions and, in parallel, an edge-based analysis is used to detect gradual transitions. Both analysis are reinforced with a motion analysis in a second step, which significantly simplifies the threshold selection problem while preserving the computational requirements. The main advantage of the proposed system is its ability to work in real time and the experimental results show high recall and precision values

Kernel bandwidth estimation for moving object detection in non-stabilized cameras

The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences

Moving Object Detection for Real-Time High-Quality Lightweight Applications on Smart Cameras

Here, a novel and efficient strategy for moving object detection by non-parametric modeling on smart cameras is presented. Whereas the background is modeled using only color information, the foreground model combines color and spatial information. The application of a particle filter allows the update of the spatial information and provides a priori information about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation result

Temporal segmentation tool for high-quality real time video editing software

The increasing use of video editing software requires faster and more efficient editing tools. As a first step, these tools perform a temporal segmentation in shots that allows a later building of indexes describing the video content. Here, we propose a novel real-time high-quality shot detection strategy, suitable for the last generation of video editing software requiring both low computational cost and high quality results. While abrupt transitions are detected through a very fast pixel-based analysis, gradual transitions are obtained from an efficient edge-based analysis. Both analyses are reinforced with a motion analysis that helps to detect and discard false detections. This motion analysis is carried out exclusively over a reduced set of candidate transitions, thus maintaining the computational requirements demanded by new applications to fulfill user needs

Robust multi-camera tracking from schematic descriptions

Although monocular 2D tracking has been largely studied in the literature, it suffers from some inherent problems, mainly when handling persistent occlusions, that limit its performance in practical situations. Tracking methods combining observations from multiple cameras seem to solve these problems. However, most multi-camera systems require detailed information from each view, making it impossible their use in real networks with low transmission rate. In this paper, we present a robust multi-camera 3D tracking method which works on schematic descriptions of the observations performed by each camera of the system, allowing thus its performance in real surveillance networks. It is based on unspecific 2D detection systems working independently in each camera, whose results are smartly combined by means of a Bayesian association method based on geometry and color, allowing the 3D tracking of the objects of the scene with a Particle Filter. The tests performed show the excellent performance of the system, even correcting possible failures of the 2D processing modules

Robust 3D multi-camera tracking from 2D mono-camera tracks by bayesian association

Visual tracking of people is essential automatic scene understanding and surveillance of areas of interest. Monocular 2D tracking has been largely studied, but it usually provides inadequate information for event nterpretation, and also proves insufficiently robust, due to view-point limitations (occlusions, etc.). In this paper, we present a light but automatic and robust 3D tracking method using multiple calibrated cameras. It is based on off-the-shelf 2D tracking systems running independently in each camera of the system, combined using Bayesian association of the monocular tracks. The proposed system shows excellent results even in challenging situations, proving itself able to automatically boost and recover from possible errors

Capabilities and limitations of mono-camera pedestrian-based autocalibration

Many environments lack enough architectural information to allow an autocalibration based on features extracted from the scene structure. Nevertheless, the observation over time of walking people can generally be used to estimate the vertical vanishing point and the horizon line in the acquired image. However, this information is not enough to allow the calibration of a general camera without presuming excessive simplifications. This paper presents a study on the capabilities and limitations of the mono-camera calibration methods based solely on the knowledge of the vertical vanishing point and the horizon line in the image. The mathematical analysis sets the conditions to assure the feasibility of the mono-camera pedestrian-based autocalibration. In addition, examples of applications are presented and discusse

Quality-optimization algorithm based on stochastic dynamic programming for MPEG DASH video streaming

In contrast to traditional push-based protocols, adaptive streaming techniques like Dynamic Adaptive Streaming over HTTP (DASH) fix attention on the client, who dynamically requests different-quality portions of the content to cope with a limited and variable bandwidth but aiming at maximizing the quality perceived by the user. Since DASH adaptation logic at the client is not covered by the standard, we propose a solution based on Stochastic Dynamic Programming (SDP) techniques to find the optimal request policies that guarantee the users' Quality of Experience (QoE). Our algorithm is evaluated in a simulated streaming session and is compared with other adaptation approaches. The results show that our proposal outperforms them in terms of QoE, requesting higher qualities on average