Aerial Cinematography Research Articles

Autonomous Aerial Filming With Distributed Lighting by a Team of Unmanned Aerial Vehicles

This letter describes a method for autonomous aerial cinematography with distributed lighting by a team of unmanned aerial vehicles (UAVs). Although camera-carrying multi-rotor helicopters have become commonplace in cinematography, their usage is limited to scenarios with sufficient natural light or of lighting provided by static artificial lights. We propose to use a formation of unmanned aerial vehicles as a tool for filming a target under illumination from various directions, which is one of the fundamental techniques of traditional cinematography. We decompose the multi-UAV trajectory optimization problem to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages, which allows us to re-plan in real time and react to changes in dynamic environments. The performance of our method has been evaluated in realistic simulation scenarios and field experiments, where we show how it increases the quality of the shots and that it is capable of planning safe trajectories even in cluttered environments.

Evolving Intelligent System for Trajectory Tracking of Unmanned Aerial Vehicles

This article develops an evolving type-2 quantum fuzzy neural network (eT2QFNN) control scheme for achieving trajectory tracking with unmanned aerial vehicles (UAVs). The proposed approach involves quantum membership functions, automatic rule growing process, and parameter adjustment learning scenario to deal with the problems of inadequacy, uncertainties, and noise in conventional control techniques. Furthermore, the proposed approach is operated in a parallel structure with the proportional derivative (PD) controller to compensate the transients in performance and learn the dynamic characteristics of the system. Besides, a sliding theory-based adaptive law is equipped with the control approach to compensate for the nonlinearity of the UAV. To assess the performance, numerical simulations and real-time experiments are carried for pitch and yaw axes control of two degrees of freedom (2DoF) helicopter test rig with the proposed approach. The simulations and experiments are aimed at achieving an offline path tracking with an objective to minimize the deviation error and improve the time response characteristics of the UAVs. The results depict the robustness of the proposed approach in terms of integral time absolute error for a helicopter following various trajectories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article addresses the problem of trajectory tracking and attitude control in a two-rotor UAV system. In practical application, there are multiple end users for an efficiently controlled UAV system. Generally, the trajectory tracking and attitude control are associated with the capability of UAVs to perform vertical take-off, landing, maneuver, and cyclic rotation as per the change in path. Furthermore, the control of UAVs for trajectory tracking and attitude provides a unified framework in efficiently following the desired path and maintaining the desired attitude. This has potential applications in the field of search and rescue operations, surveillance, military applications, environmental exploration, and aerial cinematography. Although trajectory tracking and attitude control problems have been studied a lot, the drawbacks due to uncertainties, immediate response to trajectory changes, and attitude settling are still open and challenging. This article proposed an eT2QFNN for a two rotor of UAV system with PD controller using automatic rule growing process. Sufficient trajectories are developed to make the UAV follow them under the proposed approach. Both simulation and real-time experiments were conducted and the results of the developed controller are compared with conventional approaches.

Aerial Multi-Camera Robotic Jib Crane

A formulation based on a team of unmanned aerial vehicles operating as a fully articulated multi-camera jib crane is proposed for the application of aerial cinematography. An optimization-based controller commands the formation to follow an artistic trajectory defined by the director of photography, while actively avoiding collisions and cameras' mutual visibility. The proposed scheme, based on the cluster-space formulation, presents an intuitive way of maneuvering the virtual camera fixture while automatically adjusting the motions by imposing artistic and safety constraints, facilitating the operator task.

Intermittent Contextual Learning for Keyfilter-Aware UAV Object Tracking Using Deep Convolutional Feature

Visual tracking, one of the most favorable multimedia applications, has been widely used in unmanned aerial vehicle (UAV) for civil infrastructure monitoring, aerial cinematography, autonomous navigation, etc. Most existing trackers utilize deep convolutional feature to enhance tracking robustness in scenarios of various appearance variation. However, they commonly neglect speed which is crucial for UAV with restricted calculation resources. In this work, a novel correlation filter-based keyfilter-aware tracker with a new intermittent context learning strategy is proposed to efficiently and effectively alleviate the problems of background clutter, deficient description, occlusion, illumination change, etc. Specifically, context information is utilized to empower the filter higher discriminating ability through response repression of the omnidirectional context patches. Furthermore, keyfilter is produced from the periodically selected keyframe. The latest produced keyfilter is used to restrain the current filter's corrupted changes. Most importantly, context learning of correlation filter is implemented intermittently to fully increase the tracking efficiency. This intermittent learning strategy can ensure every filter maintain context awareness owing to the restriction of keyfilter, periodically enhancing the context awareness. Substantial experiments on three challenging UAV benchmarks totally with 213 image sequences have shown that our tracker surpasses the state-of-the-art results, and exhibits a remarkable generality in short-term and long-term UAV tracking tasks as well as a variety of challenging attributes.

Visualization 3D Mosaik Picture Formation

This study aims to reveal and explore the visualization of the formation of Mosaic 3D Formation of 2,957 new students at UPN Veteran Jakarta. Visualization through the concept of Aerial Cinematography is expected to be an aesthetic enhancer in mosaic works. This mosaic is a work of art made of elements and arranged so that it becomes a 3-dimensional image or design. This research was designed as an experimental study which focused on two parts. First, try out the composition of the picture elements to form a unity that is in harmony with the overall formation movement. The second trial focused on visual exploration, which originated from the concept of aerial cinematography. The resulting mosaic visualization consists of three images with the writing of Patribera 2019, Kampus Bela Negara and PIKIR work culture logo with super high angles and long distances or called birds point of view.&#x0D;

Survey on path and view planning for UAVs

BackgroundIn recent decades, unmanned aerial vehicles (UAVs) have developed rapidly and been widely applied in many domains, including photography, reconstruction, monitoring, and search and rescue. In such applications, one key issue is path and view planning, which tells UAVs exactly where to fly and how to search. MethodsWith specific consideration for three popular UAV applications (scene reconstruction, environment exploration, and aerial cinematography), we present a survey that should assist researchers in positioning and evaluating their works in the context of existing solutions. Results/ConclusionsIt should also help newcomers and practitioners in related fields quickly gain an overview of the vast literature. In addition to the current research status, we analyze and elaborate on advantages, disadvantages, and potential explorative trends for each application domain.

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

AbstractAerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is a demand for an autonomous cinematographer that can reason about both geometry and scene context in real‐time. Existing approaches do not address all aspects of this problem; they either require high‐precision motion‐capture systems or global positioning system tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow fixed artistic guidelines specified before the flight. In this study, we address the problem in its entirety and propose a complete system for real‐time aerial cinematography that for the first time combines: (a) vision‐based target estimation; (b) 3D signed‐distance mapping for occlusion estimation; (c) efficient trajectory optimization for long time‐horizon camera motion; and (d) learning‐based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in‐depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at https://youtu.be/ookhHnqmlaU.

Shot type constraints in UAV cinematography for autonomous target tracking

During the past years, camera-equipped Unmanned Aerial Vehicles (UAVs) have revolutionized aerial cinematography, allowing easy acquisition of impressive footage. In this context, autonomous functionalities based on machine learning and computer vision modules are gaining ground. During live coverage of outdoor events, an autonomous UAV may visually track and follow a specific target of interest, under a specific desired shot type, mainly adjusted by choosing appropriate focal length and UAV/camera trajectory relative to the target. However, the selected UAV/camera trajectory and the object tracker requirements (which impose limits on the maximum allowable focal length) affect the range of feasible shot types, thus constraining cinematography planning. Therefore, this paper explores the interplay between cinematography and computer vision in the area of autonomous UAV filming. UAV target-tracking trajectories are formalized and geometrically modeled, so as to analytically compute maximum allowable focal length per scenario, to avoid 2D visual tracker failure. Based on this constraint, formulas for estimating the appropriate focal length to achieve the desired shot type in each situation are extracted, so as to determine shot feasibility. Such rules can be embedded into practical UAV intelligent shooting systems, in order to enhance their robustness by facilitating on-the-fly adjustment of the cinematography plan.

Continuous drone control using deep reinforcement learning for frontal view person shooting

Drones, also known as unmanned aerial vehicles, can be used to aid various aerial cinematography tasks. However, using drones for aerial cinematography requires the coordination of several people, increasing the cost and reducing the shooting flexibility, while also increasing the cognitive load of the drone operators. To overcome these limitations, we propose a deep reinforcement learning (RL) method for continuous fine-grained drone control, that allows for acquiring high-quality frontal view person shots. To this end, a head pose image dataset is combined with 3D models and face alignment/warping techniques to develop an RL environment that realistically simulates the effects of the drone control commands. An appropriate reward-shaping approach is also proposed to improve the stability of the employed continuous RL method. Apart from performing continuous control, it was demonstrated that the proposed method can be also effectively combined with simulation environments that support only discrete control commands, improving the control accuracy, even in this case. The effectiveness of the proposed technique is experimentally demonstrated using several quantitative and qualitative experiments.

Dronopoetics: Unmanned Aerial Cinematography and Ivan Sen’s Goldstone

Abstract The use of unmanned aerial vehicles in mainstream cinema has been underexplored in film scholarship. This article seeks to address this by looking at Ivan Sen’s use of odd aerial perspectives in his 2016 film Goldstone, captured with the use of drones. This article considers the history of aerial photography and its impacts, before analyzing three drone shots from Goldstone, in terms of what these shots contain, and how they are situated in the edit. What is proposed is a poetics of drone cinematography, as observed in Sen’s film but applicable to all visual media. This poetics considers what the drone affords the cinematographer, director, and editor in terms of perspective. Finally, Deleuze’s film-philosophy and posthuman theory are co-opted to explore what drone footage affords Ivan Sen and all filmmakers in terms of the cinematic inscription of an attitude to the environment.

Commercial Drones and the Phantom Menace

Unmanned aerial vehicles (“UAVs”) — commonly referred to as “drones” — get a lot of bad press. Their operations are portrayed as a clear and present danger to privacy and property rights — the constitutional equivalent of falsely shouting “fire” in a crowded theater for First Amendment purposes. In regular conversation, the pejorative “drone” is used instead of the proper “UAV” to connote danger, imminent and otherwise. This is understandable given the technology’s military origins. “Drones” are hunter-killer robots with scary names like “Predator” and “Reaper.” They are scary “smart” with robust intelligence, reconnaissance, and surveillance capabilities. In the “war on terror,” they stalk human beings with indefatigable persistence and kill them with “Hellfire” missiles. Unfortunately, this context overshadows the many civil and commercial uses of UAVs, including natural gas pipeline monitoring, agriculture remote sensing, and aerial cinematography. This article is the first to examine the current UAV regulatory environment as applied to the media and entertainment industry.

Aerial Cinematography

During the past six or seven years scenes involving the making of motion pictures in airplanes have been included in a number of photoplays. About a year and a half ago production was started on a war epic of the air which was to be entitled Wings and in making this picture many problems in aerial cinematography were solved.