Real-Time Computational Gigapixel Multi-Camera Systems

The standard cameras are designed to truthfully mimic the human eye and the visual system. In recent years, commercially available cameras are becoming more complex, and offer higher image resolutions than ever before. However, the quality of conventional imaging methods is limited by several parameters, such as the pixel size, lens system, the diffraction limit, etc. The rapid technological advancements, increase in the available computing power, and introduction of Graphics Processing Units (GPU) and Field-Programmable-Gate-Arrays (FPGA) open new possibilities in the computer vision and computer graphics communities. The researchers are now focusing on utilizing the immense computational power offered on the modern processing platforms, to create imaging systems with novel or significantly enhanced capabilities compared to the standard ones. One popular type of the computational imaging systems offering new possibilities is a multi-camera system. This thesis will focus on FPGA-based multi-camera systems that operate in real-time. The aim of themulti-camera systems presented in this thesis is to offer a wide field-of-view (FOV) video coverage at high frame rates. The wide FOV is achieved by constructing a panoramic image from the images acquired by the multi-camera system. Two new real-time computational imaging systems that provide new functionalities and better performance compared to conventional cameras are presented in this thesis. Each camera system design and implementation are analyzed in detail, built and tested in real-time conditions. Panoptic is a miniaturized low-cost multi-camera system that reconstructs a 360 degrees view in real-time. Since it is an easily portable system, it provides means to capture the complete surrounding light field in dynamic environment, such as when mounted on a vehicle or a flying drone. The second presented system, GigaEye II , is a modular high-resolution imaging system that introduces the concept of distributed image processing in the real-time camera systems. This thesis explains in detail howsuch concept can be efficiently used in real-time computational imaging systems. The purpose of computational imaging systems in the form of multi-camera systems does not end with real-time panoramas. The application scope of these cameras is vast. They can be used in 3D cinematography, for broadcasting live events, or for immersive telepresence experience. The final chapter of this thesis presents three potential applications of these systems: object detection and tracking, high dynamic range (HDR) imaging, and observation of multiple regions of interest. Object detection and tracking, and observation of multiple regions of interest are extremely useful and desired capabilities of surveillance systems, in security and defense industry, or in the fast-growing industry of autonomous vehicles. On the other hand, high dynamic range imaging is becoming a common option in the consumer market cameras, and the presented method allows instantaneous capture of HDR videos. Finally, this thesis concludes with the discussion of the real-time multi-camera systems, their advantages, their limitations, and the future predictions.

Beyond Cameras: Real-Time High-Resolution 3D and Panoramic MultiView Vision Systems with Their Applications

Bilal Demir

The cameras are invented by imitating the human visual system to capture the scene. The camera technologies have been substantially advanced in recent years. 108 MP resolution with 100x hybrid zoom has become standard features for smartphone flagships. In spite of the recent developments, the cameras are still restricted in terms of FoV, depth perception, pixel size, lens system, and more. That is why multicamera systems have progressively become prevalent. The multicamera systems can be a remedy to overcome the limitations that single cameras hold. The FoV can be raised to

360^\circ

through image stitching. The depth can be computed by stereo matching methods. The lens flaws and imperfections can be handled thanks to computer vision and image processing algorithms. Significant computational power is required to process a vast amount of pixels coming from multiple cameras. GPU and FPGA are promising platforms to implement computer vision and video processing applications due to their sophisticated parallelization features. FPGA platforms present more prevailing features, especially for real-time and portable vision applications. FPGAs provide less latency as they hold the connection to image sensors from a low level. FPGA enables the implementation of a system architecture dedicated to the target application. The compact systems can be designed by designing custom PCBs using only necessary ports. Also, FPGAs consume lower power and cheaper option compared to GPUs. On the other hand, GPUs propose more versatility and easy design and upgrade time. In the light of these observations, software and hardware integrated real-time high-resolution multi-view 3D and panoramic systems are presented in the scope of this thesis. Firstly, the depth estimation system is presented in the first part of the thesis. The proposed depth estimation system runs in real-time performance for up to 2K depth map resolution. The system adopts the trinocular scheme to address the occlusion problem. The pixel correspondence challenge in textureless-regions, from which the conventional stereo matching-based depth estimation systems suffer, is tackled by projecting artificial patterns through the integrated pico-projector. The application-specific system architecture ensures the high-performance depth map streaming. Secondly, the drone detection and tracking system is presented. The proposed drone detection system is capable of simultaneously monitoring 360° environment and detecting the drone from a long-range in real-time performance. The distributed architecture design enables ultra-high-resolution image processing. data coming from the hardware part. The GPU design is opted due to its high level of parallelization capability. The proposed system is appropriate to be employed for surveillance applications such as drone detection, passive radar system, vast terrain, and border control applications. Thirdly, the 3D stereoscopic panorama construction system is presented. The proposed system generated 2 separate panoramas for the left and right eyes to achieve 3D perception. The system offers the novel camera arrangement and the 3D panorama generation algorithm. The cameras are positioned to minimize the intra-panorama parallax while raising the inter-panorama parallax to augment 3D perception. Finally, the real-time vision systems are discussed with their pros and cons, and future predictions are presented in the conclusion part of the thesis.

EPFL2020

Real-time high-resolution omnidirectional imaging platform for drone detection and tracking

Bilal Demir, Selman Ergünay, Yusuf Leblebici, Gökçen Nurlu, Vladan Popovic, Jean-Philippe Thiran

Drones have become steadily affordable, which raises privacy and security concerns as well as interest in drone detection systems. On the other hand, drone detection is a challenging task due to small dimensions of drones, difficulty of long-distance detection, strict real-time constraints and necessity of wide angle coverage for drones. Although different radar and audio-assisted drone detection systems have been presented, they suffer from the cost, range, or interference problems. On the contrary, a long-range detection can be obtained by a vision-based system. Aiming that, we propose a real-time moving object detection and tracking system optimized for drone detection using 16 cameras with 20 MP resolution. The proposed system detects drones from short range and long range with 360 degrees surveillance coverage owing high-performance ultra-high-resolution (320 MP) video-processing capability. It is able to detect drones with 100 cm diameter from 700 m distance despite deceptive background. It is interference free, so multiple systems can properly operate in the vicinity without effecting each other. It integrates processing power of embedded systems with flexibility of software to generate a full platform for drone detection and tracking.

SPRINGER HEIDELBERG2019

Augmented reality for non-rigid surfaces

Julien Pilet

Augmented Reality (AR) is the process of integrating virtual elements in reality, often by mixing computer graphics into a live video stream of a real scene. It requires registration of the target object with respect to the cameras. To this end, some approaches rely on dedicated hardware, such as magnetic trackers or infra-red cameras, but they are too expensive and cumbersome to reach a large public. Others are based on specifically designed markers which usually look like bar-codes. However, they alter the look of objects to be augmented, thereby hindering their use in application for which visual design matters. Recent advances in Computer Vision have made it possible to track and detect objects by relying on natural features. However, no such method is commonly used in the AR community, because the maturity of available packages is not sufficient yet. As far as deformable surfaces are concerned, the choice is even more limited, mainly because initialization is so difficult. Our main contribution is therefore a new AR framework that can properly augment deforming surfaces in real-time. Its target platform is a standard PC and a single webcam. It does not require any complex calibration procedure, making it perfectly suitable for novice end-users. To satisfy to the most demanding application designers, our framework does not require any scene engineering, renders virtual objects illuminated by real light, and let real elements occlude virtual ones. To meet this challenge, we developed several innovative techniques. Our approach to real-time registration of a deforming surface is based on wide-baseline feature matching. However, traditional outlier elimination techniques such as RANSAC are unable to handle the non-rigid surface's large number of degrees of freedom. We therefore proposed a new robust estimation scheme that allows both 2–D and 3–D non-rigid surface registration. Another issue of critical importance in AR to achieve realism is illumination handling, for which existing techniques often require setup procedures or devices such as reflective spheres. By contrast, our framework includes methods to estimate illumination for rendering purposes without sacrificing ease of use. Finally, several existing approaches to handling occlusions in AR rely on multiple cameras or can only deal with occluding objects modeled beforehand. Our requires only one camera and models occluding objects at runtime. We incorporated these components in a consistent and flexible framework. We used it to augment many different objects such as a deforming T-shirt or a sheet of paper, under challenging conditions, in real-time, and with correct handling of illumination and occlusions. We also used our non-rigid surface registration technique to measure the shape of deformed sails. We validated the ease of deployment of our framework by distributing a software package and letting an artist use it to create two AR applications.

EPFL2008