Fernando Jaureguizar

Rate control and bit allocation for MPEG-4

In previous years, an interest has developed in the coded representations of video signals allowing independent manipulation of semantically independent elements (objects). Along these lines, the ISO standard MPEG-4 enhances the traditional concept of the video sequence to convert it into a synchronized set of visual objects organized in a flexible way. The real-time generation of a bitstream according to this new paradigm, and suitable for its transmission through either fixed- or variable-rate channels, results in a challenging new bit allocation and rate control problem, which has to satisfy complex application requirements. This paper formalizes this new issue by focusing on the design of rate control systems for real-time applications. The proposed approach relies on the modelization of the source and the optimization of a cost criterion based on signal quality parameters. Different cost criteria are provided, corresponding to a set of relevant definitions of the object priority concept. Algorithms are introduced to minimize the average distortion of the objects, to guarantee desired qualities to the most relevant ones, and to keep constant ratios among the object qualities. The techniques have been applied to a coder implementing the MPEG-4 video verification model, showing good properties in terms of achievement of the control objectives.

Stabilization of Inverse Perspective Mapping Images based on Robust Vanishing Point Estimation

In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust estimation of the vanishing point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The estimation of the vanishing point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms.

An efficient multiple object detection and tracking framework for automatic counting and video surveillance applications

Automatic visual object counting and video surveillance have important applications for home and business environments, such as security and management of access points. However, in order to obtain a satisfactory performance these technologies need professional and expensive hardware, complex installations and setups, and the supervision of qualified workers. In this paper, an efficient visual detection and tracking framework is proposed for the tasks of object counting and surveillance, which meets the requirements of the consumer electronics: off-the-shelf equipment, easy installation and configuration, and unsupervised working conditions. This is accomplished by a novel Bayesian tracking model that can manage multimodal distributions without explicitly computing the association between tracked objects and detections. In addition, it is robust to erroneous, distorted and missing detections. The proposed algorithm is compared with a recent work, also focused on consumer electronics, proving its superior performance.

Robust multiple lane road modeling based on perspective analysis

Road modeling is the first step towards environment perception within driver assistance video-based systems. Typically, lane modeling allows applications such as lane departure warning or lane invasion by other vehicles. In this paper, a new monocular image processing strategy that achieves a robust multiple lane model is proposed. The identification of multiple lanes is done by firstly detecting the own lane and estimating its geometry under perspective distortion. The perspective analysis and curve fitting allows to hypothesize adjacent lanes assuming some a priori knowledge about the road. The verification of these hypotheses is carried out by a confidence level analysis. Several types of sequences have been tested, with different illumination conditions, presence of shadows and significant curvature, all performing in realtime. Results show the robustness of the system, delivering accurate multiple lane road models in most situations.

On-board robust vehicle detection and tracking using adaptive quality evaluation

This paper presents a robust method for real-time vehicle detection and tracking in dynamic traffic environments. The proposed strategy aims to find a trade-off between the robustness shown by time-uncorrelated detection techniques and the speed-up obtained with tracking algorithms. It combines both advantages by continuously evaluating the quality of the tracking results along time and triggering new detections to restart the tracking process when quality falls behind a certain quality requirement. Robustness is also ensured within the tracking algorithm with an outlier rejection stage and the use of stochastic filtering. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections.

Subjective assessment of the impact of transmission errors in 3DTV compared to HDTV

Recently, broadcasted 3D video content has reached households with the first generation of 3DTV. However, few studies have been done to analyze the Quality of Experience (QoE) perceived by the end-users in this scenario. This paper studies the impact of transmission errors in 3DTV, considering that the video is delivered in side-by-side format over a conventional packet-based network. For this purpose, a novel evaluation methodology based on standard single stimulus methods and with the aim of keeping as close as possible the home environment viewing conditions has been proposed. The effects of packet losses in monoscopic and stereoscopic videos are compared from the results of subjective assessment tests. Other aspects were also measured concerning 3D content as naturalness, sense of presence and visual fatigue. The results show that although the final perceived QoE is acceptable, some errors cause important binocular rivalry, and therefore, substantial visual discomfort.

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

Hand-gesture recognition system based on color imagery for HCI.Design of a novel spatio-temporal descriptor with a high discriminative power.Sensible combination of spatial (local and global) and temporal information.Obtained results outperform other relevant works using depth and color imagery. A more natural, intuitive, user-friendly, and less intrusive Human-Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

Robust 3D people tracking and positioning system in a semi-overlapped multi-camera environment

People positioning and tracking in 3D indoor environments are challenging tasks due to background clutter and occlusions. Current works are focused on solving people occlusions in low-cluttered backgrounds, but fail in high-cluttered scenarios, specially when foreground objects occlude people. In this paper, a novel 3D people positioning and tracking system is presented, which shows itself robust to both possible occlusion sources: static scene objects and other people. The system holds on a set of multiple cameras with partially overlapped fields of view. Moving regions are segmented independently in each camera stream by means of a new background modeling strategy based on Gabor filters. People detection is carried out on these segmentations through a template-based correlation strategy. Detected people are tracked independently in each camera view by means of a graph-based matching strategy, which estimates the best correspondences between consecutive people segmentations. Finally, 3D tracking and positioning of people is achieved by geometrical consistency analysis over the tracked 2D candidates, using head position (instead of object centroids) to increase robustness to foreground occlusions.

A video-aware FEC-based unequal loss protection system for video streaming over RTP

A video-aware unequal loss protection (ULP) system for protecting RTP video streaming in bursty packet loss networks is proposed. Considering the relevance of the frame, the state of the channel, and the bitrate constraints of the protection bitstream, our algorithm selects in real time the most suitable frames to be protected through forward error protection (FEC) techniques. It benefits from a wise RTP encapsulation that allows working at a frame level without requiring any further process than that of parsing RTP headers. This makes our system straightforward and fast, perfectly suitable to be included in commercial video streaming servers. Simulation results show how our technique outperforms other proposed ULP schemes.

Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.