FVV Live is a novel end-to-end free-viewpoint video system, designed for real-time operation, using consumer-grade cameras and hardware, which enables low deployment costs and easy installation for immersive event-broadcasting or videoconferencing. All the blocks of the system have been designed to maximize perceptual video quality, overcoming the limitations imposed by hardware and network, which impact directly the accuracy of depth data and thus the quality of virtual view synthesis. Therefore, it does not sacrifice perceptual video quality with respect to high-end counterparts. The results presented in this paper correspond to an implementation with nine stereo-based depth cameras. However, the design of the acquisition block of FVV Live allows scalability for an arbitrary number of cameras. In addition, FVV Live presents low motion-to-photon and end-to-end delays, which enables a responsive free-viewpoint navigation and bilateral immersive communications. Moreover, the visual quality of FVV Live has been assessed through subjective assessment with satisfactory results, and additional comparative tests show that it is preferred over state-of-the-art DIBR alternatives.

In the current worldwide situation, pedestrian detection has reemerged as a pivotal tool for intelligent video-based systems aiming to solve tasks such as pedestrian tracking, social distancing monitoring or pedestrian mass counting. Pedestrian detection methods, even the top performing ones, are highly sensitive to occlusions among pedestrians, which dramatically degrades their performance in crowded scenarios. The generalization of multi-camera setups permits to better confront occlusions by combining information from different viewpoints. In this paper, we present a multi-camera approach to globally combine pedestrian detections leveraging automatically extracted scene context. Contrarily to the majority of the methods of the state-of-the-art, the proposed approach is scene-agnostic, not requiring a tailored adaptation to the target scenario–e.g., via fine-tuning. This noteworthy attribute does not require ad hoc training with labeled data, expediting the deployment of the proposed method in real-world situations. Context information, obtained via semantic segmentation, is used (1) to automatically generate a common area of interest for the scene and all the cameras, avoiding the usual need of manually defining it, and (2) to obtain detections for each camera by solving a global optimization problem that maximizes coherence of detections both in each 2D image and in the 3D scene. This process yields tightly fitted bounding boxes that circumvent occlusions or miss detections. The experimental results on five publicly available datasets show that the proposed approach outperforms state-of-the-art multi-camera pedestrian detectors, even some specifically trained on the target scenario, signifying the versatility and robustness of the proposed method without requiring ad hoc annotations nor human-guided configuration.

Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose estimation, etc. This association task is typically modeled as a bipartite graph matching problem and often solved by applying minimum-cost flow techniques, which may be computationally demanding for large data. Furthermore, cameras are usually treated by pairs, obtaining local solutions, rather than finding a global solution at once for all multiple cameras. Other key issue is that of the affinity function: the widespread usage of non-learnable pre-defined distances, such as the Euclidean and Cosine ones. This paper proposes an effective approach for cross-camera data-association focused on a global solution, instead of processing cameras by pairs. To avoid the usage of fixed distances and thresholds, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity functions. We validate the proposal for pedestrian cross-camera association, showing results over the EPFL multi-camera pedestrian dataset. Our approach considerably outperforms the literature data association techniques, without requiring to be trained in the same scenario in which it is tested. Our code is available at http://www-vpu.eps.uam.es/publications/gnn_cca.

We describe a network congestion control mechanism for cloud gaming (CG) platforms based on the user datagram protocol (UDP). To minimize the contribution of the downstream transmission delay to the total end-to-end latency in the interaction-perception loop, we first define the round-trip video latency (RTVL) and develop a congestion model. Based on them, we design and implement an adaptation strategy that detects the early stages of congestion to prevent high values of RTVL and network bufferbloat, thus avoiding packet losses. Using data measured from the network, our strategy modifies the target output bitrate of the video encoder to throttle down or upto the data flow sent by the server to the client. In the presence of sudden downstream channel capacity drops of over 40%, our algorithm reactively manages to satisfy the key CG requirements for interactive games by entirely avoiding the packet losses and keeping the RTVL below 100 ms. In reasonably stable network conditions, our algorithm proactively keeps exploring for higher bitrates and building a “network state dictionary,” due to which it achieves an effective downstream channel capacity use of ~95%.

The subjective assessment of super multiview (SMV) video considers two main perceptual factors: image quality and visual comfort at the viewpoint transition. While previous works only covered raw content with high levels of visual comfort, this work supersedes them by targeting the subjective assessment of SMV content with coding artifacts. The outcome of this analysis yields important conclusions regarding the relationship between these two factors, indicating that the perceived image quality is independent from the view point change speed, and the perceived visual comfort at the view point transition is independent from the image quality. These conclusions facilitate the extension of the scope of existing subjective perception models, designed for raw SMV content, to coded content.