Panoramic Vision Transformer for Saliency Detection in 360{\deg} Videos

360$^\circ$ video saliency detection is one of the challenging benchmarks for 360$^\circ$ video understanding since non-negligible distortion and discontinuity occur in the projection of any format of 360$^\circ$ videos, and capture-worthy viewpoint in the omnidirectional sphere is ambiguous by nature. We present a new framework named Panoramic Vision Transformer (PAVER). We design the encoder using Vision Transformer with deformable convolution, which enables us not only to plug pretrained models from normal videos into our architecture without additional modules or finetuning but also to perform geometric approximation only once, unlike previous deep CNN-based approaches. Thanks to its powerful encoder, PAVER can learn the saliency from three simple relative relations among local patch features, outperforming state-of-the-art models for the Wild360 benchmark by large margins without supervision or auxiliary information like class activation. We demonstrate the utility of our saliency prediction model with the omnidirectional video quality assessment task in VQA-ODV, where we consistently improve performance without any form of supervision, including head movement.Comment: Published to ECCV202

A dataset of annotated omnidirectional videos for distancing applications

Omnidirectional (or 360◦ ) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360◦ videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360◦ image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications

WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders

Imaging methods for understanding and improving visual training in the geosciences

Experience in the field is a critical educational component of every student studying geology. However, it is typically difficult to ensure that every student gets the necessary experience because of monetary and scheduling limitations. Thus, we proposed to create a virtual field trip based off of an existing 10-day field trip to California taken as part of an undergraduate geology course at the University of Rochester. To assess the effectiveness of this approach, we also proposed to analyze the learning and observation processes of both students and experts during the real and virtual field trips. At sites intended for inclusion in the virtual field trip, we captured gigapixel resolution panoramas by taking hundreds of images using custom built robotic imaging systems. We gathered data to analyze the learning process by fitting each geology student and expert with a portable eye- tracking system that records a video of their eye movements and a video of the scene they are observing. An important component of analyzing the eye-tracking data requires mapping the gaze of each observer into a common reference frame. We have made progress towards developing a software tool that helps automate this procedure by using image feature tracking and registration methods to map the scene video frames from each eye-tracker onto a reference panorama for each site. For the purpose of creating a virtual field trip, we have a large scale semi-immersive display system that consists of four tiled projectors, which have been colorimetrically and photometrically calibrated, and a curved widescreen display surface. We use this system to present the previously captured panoramas, which simulates the experience of visiting the sites in person. In terms of broader geology education and outreach, we have created an interactive website that uses Google Earth as the interface for visually exploring the panoramas captured for each site

Multi-Object Detection, Pose Estimation and Tracking in Panoramic Monocular Imagery for Autonomous Vehicle Perception

While active sensing such as radars, laser-based ranging (LiDAR) and ultrasonic sensors are nearly ubiquitous in modern autonomous vehicle prototypes, cameras are more versatile because they are nonetheless essential for tasks such as road marking detection and road sign reading. Active sensing technologies are widely used because active sensors are, by nature, usually more reliable than cameras to detect objects, however they are lower resolution, break in challenging environmental conditions such as rain and heavy reflections, as well as materials such as black paint. Therefore, in this work, we focus primarily on passive sensing technologies. More specifically, we look at monocular imagery and to what extent, it can be used as replacement for more complex sensing systems such as stereo, multi-view cameras and LiDAR. Whilst the main strength of LiDAR is its ability to measure distances and naturally enable 3D reasoning; in contrast, camera-based object detection is typically restricted to the 2D image space. We propose a convolutional neural network extending object detection to estimate the 3D pose and velocity of objects from a single monocular camera. Our approach is based on a siamese neural network able to process pair of video frames to integrate temporal information. While the prior work has focused almost exclusively on the processing of forward-facing rectified rectilinear vehicle mounted cameras, there are no studies of panoramic imagery in the context of autonomous driving. We introduce an approach to adapt existing convolutional neural networks to unseen 360° panoramic imagery using domain adaptation via style transfer. We also introduce a new synthetic evaluation dataset and benchmark for 3D object detection and depth estimation in automotive panoramic imagery. Multi-object tracking-by-detection is often split into two parts: a detector and a tracker. In contrast, we investigate the use of end-to-end recurrent convolutional networks to process automotive video sequences to jointly detect and track objects through time. We present a multitask neural network able to track online the 3D pose of objects in panoramic video sequences. Our work highlights that monocular imagery, in conjunction with the proposed algorithmic approaches, can offer an effective replacement for more expensive active sensors to estimate depth, to estimate and track the 3D pose of objects surrounding the ego-vehicle; thus demonstrating that autonomous driving could be achieved using a limited number of cameras or even a single 360° panoramic camera, akin to a human driver perception

Videos in Context for Telecommunication and Spatial Browsing

The research presented in this thesis explores the use of videos embedded in panoramic imagery to transmit spatial and temporal information describing remote environments and their dynamics. Virtual environments (VEs) through which users can explore remote locations are rapidly emerging as a popular medium of presence and remote collaboration. However, capturing visual representation of locations to be used in VEs is usually a tedious process that requires either manual modelling of environments or the employment of specific hardware. Capturing environment dynamics is not straightforward either, and it is usually performed through specific tracking hardware. Similarly, browsing large unstructured video-collections with available tools is difficult, as the abundance of spatial and temporal information makes them hard to comprehend. At the same time, on a spectrum between 3D VEs and 2D images, panoramas lie in between, as they offer the same 2D images accessibility while preserving 3D virtual environments surrounding representation. For this reason, panoramas are an attractive basis for videoconferencing and browsing tools as they can relate several videos temporally and spatially. This research explores methods to acquire, fuse, render and stream data coming from heterogeneous cameras, with the help of panoramic imagery. Three distinct but interrelated questions are addressed. First, the thesis considers how spatially localised video can be used to increase the spatial information transmitted during video mediated communication, and if this improves quality of communication. Second, the research asks whether videos in panoramic context can be used to convey spatial and temporal information of a remote place and the dynamics within, and if this improves users' performance in tasks that require spatio-temporal thinking. Finally, the thesis considers whether there is an impact of display type on reasoning about events within videos in panoramic context. These research questions were investigated over three experiments, covering scenarios common to computer-supported cooperative work and video browsing. To support the investigation, two distinct video+context systems were developed. The first telecommunication experiment compared our videos in context interface with fully-panoramic video and conventional webcam video conferencing in an object placement scenario. The second experiment investigated the impact of videos in panoramic context on quality of spatio-temporal thinking during localization tasks. To support the experiment, a novel interface to video-collection in panoramic context was developed and compared with common video-browsing tools. The final experimental study investigated the impact of display type on reasoning about events. The study explored three adaptations of our video-collection interface to three display types. The overall conclusion is that videos in panoramic context offer a valid solution to spatio-temporal exploration of remote locations. Our approach presents a richer visual representation in terms of space and time than standard tools, showing that providing panoramic contexts to video collections makes spatio-temporal tasks easier. To this end, videos in context are suitable alternative to more difficult, and often expensive solutions. These findings are beneficial to many applications, including teleconferencing, virtual tourism and remote assistance

ScanGAN360: a generative model of realistic scanpaths for 360 images

Understanding and modeling the dynamics of human gaze behavior in 360° environments is crucial for creating, improving, and developing emerging virtual reality applications. However, recruiting human observers and acquiring enough data to analyze their behavior when exploring virtual environments requires complex hardware and software setups, and can be time-consuming. Being able to generate virtual observers can help overcome this limitation, and thus stands as an open problem in this medium. Particularly, generative adversarial approaches could alleviate this challenge by generating a large number of scanpaths that reproduce human behavior when observing new scenes, essentially mimicking virtual observers. However, existing methods for scanpath generation do not adequately predict realistic scanpaths for 360° images. We present ScanGAN360, a new generative adversarial approach to address this problem. We propose a novel loss function based on dynamic time warping and tailor our network to the specifics of 360° images. The quality of our generated scanpaths outperforms competing approaches by a large margin, and is almost on par with the human baseline. ScanGAN360 allows fast simulation of large numbers of virtual observers, whose behavior mimics real users, enabling a better understanding of gaze behavior, facilitating experimentation, and aiding novel applications in virtual reality and beyond

Object Tracking in Panoramic Video

Tato diplomová práce mapuje dosavadní vývoj v oblasti sledování objektů v panoramatickém 360° videu. Práce si klade za cíl odhalit hlavní problémy, které souvisejí se sledováním objektů a dále se zaměřuje na jejich řešení v rámci panoramatického videa. Během studia této problematiky bylo zjištěno, že dosud bylo provedeno jen velmi málo řešení pro sledování objektů v ekvirektangulární projekci panoramatického videa. V této práci jsou proto představena dvě vylepšení pro metody sledování jediného objektu založené na adaptaci ekvirektangulárních snímků. Tato diplomová práce navíc přináší vlastní manuálně vytvořený dataset panoramatických videí s více než 9900 anotacemi. Pro tento nový dataset je provedeno detailní vyhodnocení celkově 12 významných i moderních algoritmů pro sledování objektů.The master thesis maps the state of the art of visual object tracking in panoramic 360° video. The thesis aims to reveal the main problems related to visual object tracking and moreover focuses on their solution in panoramic videos. In the study of the existing approaches was found that very few solutions of visual object tracking in equirectangular projection of panoramic video have been implemented so far. This thesis therefore presents two improvements of object tracking methods that are based on the adaptation of equirectangular frames. In addition, this thesis brings the manually created dataset of panoramic videos with more than 9900 annotations. Finally the detailed evaluation of 12 well known and state of the art trackers has been performed for this new dataset.