We introduce GS-SFS, a method that utilizes a camera array with wide baselines for high-quality multiple human mesh reconstruction in large-scale sports scenes. Traditional human reconstruction methods in sports scenes, such as Shape-from-Silhouette (SFS), struggle with sparse camera setups and small human targets, making it challenging to obtain complete and accurate human representations. Despite advances in differentiable rendering, including 3D Gaussian Splatting (3DGS), which can produce photorealistic novel-view renderings with dense inputs, accurate depiction of surfaces and generation of detailed meshes is still challenging. Our approach uniquely combines 3DGS’s view synthesis with an optimized SFS method, thereby significantly enhancing the quality of multiperson mesh reconstruction in large-scale sports scenes. Specifically, we introduce body shape priors, including the human surface point clouds extracted through SFS and human silhouettes, to constrain 3DGS to a more accurate representation of the human body only. Then, we develop an improved mesh reconstruction method based on SFS, mainly by adding additional viewpoints through 3DGS and obtaining a more accurate surface to achieve higher-quality reconstruction models. We implement a high-density scene resampling strategy based on spherical sampling of human bounding boxes and render new perspectives using 3D Gaussian Splatting to create precise and dense multi-view human silhouettes. During mesh reconstruction, we integrate the human body’s 2D Signed Distance Function (SDF) into the computation of the SFS’s implicit surface field, resulting in smoother and more accurate surfaces. Moreover, we enhance mesh texture mapping by blending original and rendered images with different weights, preserving high-quality textures while compensating for missing details. The experimental results from real basketball game scenarios demonstrate the significant improvements of our approach for multiple human body model reconstruction in complex sports settings.

Estimating the 3-D human poses of multiple individuals using multiple cameras is a significant research topic within the field of vision-based measurement. Contrary to the classical outside-in camera capture system, the inside-out panoramic camera capture system can cover larger scenes with fewer cameras for 3-D human pose estimation. This advancement extends the application of 3-D human pose measurement beyond small spaces like motion capture studios. For example, this approach can be utilized for intelligent security surveillance in broad outdoor squares or capturing athletes’ movements in large-scale sports scenes. However, existing inside-out 3-D human pose estimation methods that utilize panoramic cameras encounter challenges, particularly in multiperson occlusion scenarios. Aimed at these problems, this article presents a novel inside-out multiperson 3-D human pose estimation method using just a few calibrated panoramic cameras. Specifically, we first propose a cross-view multiperson matching algorithm based on panoramic camera epipolar geometry constraint to improve human body matching robustness across viewpoints. Then, we take advantage of multiple panoramic cameras and introduce a multiview human pose clustering and fusion algorithm to improve the average recall (AR) of 3-D human pose estimation. In addition, we propose a multiview human pose nonlinear optimization algorithm to jointly optimize the weighted reprojection errors of estimated 3-D human poses, which can further improve the average precision (AP). We have conducted extensive experiments on the public Panoptic Studio dataset and self-built real and simulated datasets to demonstrate that our method can inside-out estimate 3-D human poses using multiple panoramic cameras. Compared to state-of-the-art methods, the 3-D human pose omission is greatly reduced through the complementarity of multiple cameras, and the precision of 3-D human pose estimation is largely improved by utilizing multicamera observation information.

Free-viewpoint videos offer audiences a more immersive and liberated way to watch sports. The rendering of sports scenes encompasses two essential elements: dynamic targets and static scenes. While much current research focuses on achieving high-quality rendering of human bodies, rendering large-scale sports scenes presents various challenges. Sports arenas are characterized by large spatial extents, restricted camera placement, uncontrollable lighting, weak textures, and repetitive patterns, all of which pose significant obstacles to achieving high-quality scene rendering. In this work, we propose a neural radiance field rendering method based on scene-prior geometric constraints. We introduce prior 3-D geometric dimensions and 2-D semantic masks, to derive high-precision ground plane depth maps from camera imaging parameters. This is a geometry-based method that does not rely on visual features, and thus, it is unaffected by insufficient textures, repetition, and reflections. Subsequently, we apply ground depth maps as geometric consistency constraints to optimize the neural rendering network, thereby reducing the impact of color inconsistencies across viewpoints. The visual sensor network we build can synchronously capture static fields and dynamic targets in sports scenes. Based on the visual sensor network, we collected multiviewpoint datasets of large-scale sports scenes at Invengo and Xidian Gymnasium for performance evaluation. Experimental results demonstrate that our method can generate high-precision and cross-viewpoint scale-consistent depth constraints and helps reduce holes and artifacts in synthesized views. Our method outperforms the state of the art (SOTA) for novel view rendering for challenging large-scale sports scenes.

Free-viewpoint video allows the user to view objects from any virtual perspective, creating an immersive visual experience. This technology enhances the interactivity and freedom of multimedia performance. However, many free-viewpoint video synthesis methods are hard to satisfy the requirements of real-time and high precision, particularly in sports competitions with large areas and numerous objects. To address these issues, we propose a free-viewpoint video synthesis method based on distance field acceleration. The central idea is to fuse multi-view distance field information and use it to adjust the search step size adaptively. Adaptive step size search is applied in two aspects: fast estimation of multi-object three-dimensional surfaces and synthetic view rendering based on global occlusion judgement. And we implement parallel computing and interactive display of this method on the CUDA and OpenGL interoperability frameworks. Afterward, we build real-world and simulated experimental platforms to obtain sufficient multi-view image datasets and evaluate the performance of our method. Experimental results show that the proposed method can render freeviewpoint videos with multiple objects in large-scale sports fields at 25 fps. Furthermore, the visual quality of our synthetic novel viewpoint images outperforms state-of-the-art neural-rendering-based methods.

Achieving precise synchronization is critical for multi-camera systems in various applications. Traditional methods rely on hardware-triggered synchronization, necessitating significant manual effort to connect and adjust synchronization cables, especially with multiple cameras involved. This not only increases labor costs but also restricts scene layout and incurs high setup expenses. To address these challenges, we propose a novel subframe synchronization technique for multi-camera systems that operates without the need for additional hardware triggers. Our approach leverages a time-calibrated video featuring specific markers and a uniformly moving ball to accurately extract the temporal relationship between local and global time systems across cameras. This allows for the calculation of new timestamps and precise frame-level alignment. By employing interpolation algorithms, we further refine synchronization to the subframe level. Experimental results validate the robustness and high temporal precision of our method, demonstrating its adaptability and potential for use in demanding multi-camera setups.

Continuous, robust, and precise localization is pivotal in enabling the autonomous operation of robots and aircraft in intricate environments, particularly in the absence of GNSS (global navigation satellite system) signals. However, commonly employed approaches, such as visual odometry and inertial navigation systems, encounter hindrances in achieving effective navigation and positioning due to issues of error accumulation. Additionally, the challenge of managing extensive map creation and exploration arises when deploying these systems on unmanned aerial vehicle terminals. This study introduces an innovative system capable of conducting long-range and multi-map visual SLAM (simultaneous localization and mapping) using monocular cameras equipped with pinhole and fisheye lens models. We formulate a graph optimization model integrating GNSS data and graphical information through multi-sensor fusion navigation and positioning technology. We propose partitioning SLAM maps based on map health status to augment accuracy and resilience in large-scale map generation. We introduce a multi-map matching and fusion algorithm leveraging geographical positioning and visual data to address excessive discrete mapping, leading to resource wastage and reduced map-switching efficiency. Furthermore, a multi-map-based visual SLAM online localization algorithm is presented, adeptly managing and coordinating distinct geographical maps in different temporal and spatial domains. We employ a quadcopter to establish a testing system and generate an aerial image dataset spanning several kilometers. Our experiments exhibit the framework’s noteworthy robustness and accuracy in long-distance navigation. For instance, our GNSS-assisted multi-map SLAM achieves an average accuracy of 1.5 m within a 20 km range during unmanned aerial vehicle flights.

Geolocating multiple targets of interest on the ground from an aerial platform is an important activity in many applications, such as visual surveillance. However, due to the limited measurement accuracy of commonly used airborne sensors (including altimeters, accelerometer, gyroscopes, etc.) and the small size, complex motion, and a large number of ground targets in aerial images, most of the current unmanned aerial vehicle (UAV)-based ground target geolocation algorithms have difficulty obtaining accurate geographic location coordinates online, especially at middle and high altitudes. To solve these problems, in this paper, a novel online ground multitarget geolocation framework using a UAV platform is proposed, which minimizes the introduction of sensor error sources and uses only monocular aerial image sequences and Global Positioning System (GPS) data to perform parallel processing of target detection and rapid three-dimensional (3D) sparse geographic map construction and target geographic location estimations, thereby improving the accuracy and speed of ground multitarget online geolocation. In this framework, a detection algorithm based on deep learning is first adopted to improve the accuracy and robustness of small target detection in aerial images by constructing an aerial image dataset. Then, we propose a novel target geolocation algorithm based on 3D map construction, which combines continuous images and GPS data collected online using a UAV platform to generate a 3D geographic map and accurately estimates the GPS location of the target center pixel through projection and triangulation of the map points on the image. Finally, we design a data transmission architecture that selects multiple processes to perform image acquisition, target detection and target geolocation tasks in parallel and utilizes database communication between the processes to achieve accurate online geolocation of ground targets in aerial images, regardless of whether the target is in a static or moving state. To evaluate the effectiveness of the proposed framework, we build an online ground multitarget geolocation system using a quad-rotor UAV and carry out a large number of experiments in simulations and real environments. Qualitative and quantitative experimental results proved that the framework can accurately locate ground targets in various complex environments, such as parks, highways, schools, cities, different flight altitudes (50–2000m) and different attitude angles, and the average positioning error is approximately 1 m at 2000 m for cities with rich 3D structures.

In this paper, we propose a novel calibration-free cross-camera target association algorithm that aims to relate local visual data of the same object across cameras with overlapping FOVs. Unlike other methods using object's own characteristics, our approach makes full use of the interactions between objects and explores their spatiotemporal consistency in projection transformation to associate cameras. It has wider applicability in deployed overlapping multi-camera systems with unknown or rarely available calibration data, especially if there is a large perspective gap between cameras. Specifically, we first extract trajectory intersection which is one of the typical object-object interactive behaviors from each camera for feature vector construction. Then, based on the consistency of object-object interactions, we propose a multi-camera spatiotemporal alignment method via wide-domain cross-correlation analysis. It realizes time synchronization and spatial calibration of the multi-camera system simultaneously. After that, we introduce a cross-camera target association approach using aligned object-object interactions. The local data of the same target are successfully associated across cameras without any additional calibration. Extensive experimental evaluations on different databases verify the effectiveness and robustness of our proposed method.

Multicamera calibration is an important technique for generating free-view video. By arranging multiple cameras in a scene after camera calibration and image processing, a multidimensional viewing experience can be presented to the audience. To address the problem that low texture cannot be robustly self-calibrated in common sports scenes when placing artificial markers or towers in the calibration process is impractical, this article proposes a robust multicamera calibration method based on sequence feature matching and fusion. Additionally, to validate the effectiveness of the proposed calibration algorithm, a virtual axis fast bullet-time synthesis algorithm is proposed for generating a free-view video. First, camera self-calibration is performed in low-texture situations by fusing dynamic objects in time series to enrich geometric constraints in scenes without the use of calibration panels or additional artificial markers. Second, a virtual-axis bullet-time video synthesis method based on the calibration result is proposed. In the calibrated multicamera scenario, a fast bullet-time video is generated by constructing a virtual axis. Qualitative and quantitative experiments in comparison with a state-of-the-art calibration method demonstrate the validity and robustness of the proposed calibration algorithm for free-view video synthesis tasks.

Bullet-time videos have been widely used in movies, TV advertisements, and computer games, and can produce an immersive and smooth orbital free-viewpoint of frozen action. However, existing bullet-time video synthesis methods remain challenging in practical applications, especially in complex situations with poor camera calibration and a variety of camera array structures. This paper proposes a novel bullet-time video synthesis method based on a virtual dynamic target axis. We adopt an image similarity transformation strategy to eliminate image distortion in the bullet-time video. We use a high-order polynomial curve fitting strategy to reserve more bullet-time video frame content. The proposed dynamic target axis strategy can support various camera array structures, including camera arrays with and without a common field of view. In addition, this strategy can also tolerate poor camera calibration situations with unevenly distributed reprojection errors to some extent and synthesize smooth bullet-time videos without high-precision camera calibration. Qualitative and quantitative experiments in real environments and on simulation platforms demonstrate the high performance of our bullet-time video synthesis method. Compared with the state-of-the-art methods, the proposed method shows superiority.

Limited by aircraft flight altitude and camera parameters, it is necessary to obtain wide-angle panoramas quickly by stitching aerial images, which is helpful in rapid disaster investigation, recovery after earthquakes, and battlefield reconnaissance. However, most existing stitching algorithms do not meet practical real-time, robustness, and accuracy requirements simultaneously, especially in the case of a long-distance multi-strip flight. In this paper, we propose a novel image-only real-time UAV image mosaic framework for long-distance multi-strip flights, which does not require any auxiliary information such as GPS or GCPs...

The construction of smart city makes information interconnection play an increasingly important role in intelligent surveillance systems. Especially the interconnection among massive cameras is the key to realizing the evolution from current fragmented monitoring to interconnection surveillance. However, it remains a challenging problem in practical systems due to large sensor quantity, various camera types, and complex spatial layout. Aimed at this problem, this paper proposes a novel multi-camera joint spatial self-organization approach, which realizes interconnection surveillance by unifying cameras into one imaging space. Differing from existing back-end data association strategy, our method takes front-end data calibration as a breakthrough to relate surveillance data ...

With the extensive application of robots, such as unmanned aerial vehicle (UAV) in exploring unknown environments, visual odometry (VO) algorithms have played an increasingly important role. The environments are diverse, not always textured, or low-textured with insufficient features, making them challenging for mainstream VO. However, for low-texture environment, due to the structural characteristics of man-made scene, the lines are usually abundant. In this paper, we propose a virtual-real hybrid map based monocular visual odometry algorithm. The core idea is that we reprocess line segment features to generate the virtual intersection matching points, which can be used to build the virtual map. Introducing virtual map can improve the stability of the visual odometry algorithm in low-texture environment. Specifically, we first combine unparallel matched line segments to generate virtual intersection matching points, then, based on the virtual intersection matching points, we triangulate to get a virtual map, combined with the real map built upon the ordinary point features to form a virtual-real hybrid 3D map. Finally, using the hybrid map, the continuous camera pose estimation can be solved. Extensive experimental results have demonstrated the robustness and effectiveness of the proposed method in various low-texture scenes ...

In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to ...

Abstract: In recent years, small unmanned aerial vehicles (UAVs) have rapidly developed and are widely used in disaster relief, traffic monitoring and military surveillance. In order to better perform these tasks, it is necessary to improve the environmental perception ability of UAV in dynamic environment including static and dynamic perception ability. Specifically, 3D reconstruction for static scene and localization for moving object are both required. Simultaneous Localization And Mapping (SLAM) technology has made great progress in static scene structure reconstruction and UAV self motion estimation. However, accurate real-time localization of ...

Synthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observation angle and foreground de-focus blur. But the existence of them is frequently a source of perspective effect decrease and occluded object imaging quality degradation. In order to solve this problem, we propose a novel data-driven variable synthetic aperture imaging based on semantic feedback...

In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos...

With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV...

Tracking multiple people in crowds is a fundamental and essential task in the multimedia field. It is often hindered by difficulties such as dynamic occlusion between objects, cluttered background and abrupt illumination changes. To respond to this need, in this paper, we combine deep and depth to build a stereo tracking system for crowds. The core of the system is the fusion of the advantages of deep learning and depth information, which is exploited to achieve object segmentation and improve the multiobject tracking performance in severe occlusion...

In recent years, unmanned aerial vehicles (UAVs) have rapidly developed, but the illegal use of UAVs by civilians has resulted in disorder and security risks and has increasingly triggered community concern and worry. Therefore, the monitoring and recycling of UAVs in key regions is of great significance. This paper presents a novel panoramic UAV surveillance and autonomous recycling system that is based on an unique structure-free fisheye camera array and has the capability of real-time UAV detection...

With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remote sensing, 3D reconstruction using aerial images has been a vibrant area of research. However, fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale means that the number and resolution of images are enormous, which brings significant computational cost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In this paper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchically merges partial structures...

Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions...

With the rapid development of Unmanned Aerial Vehicle (UAV) systems, the autonomous landing of a UAV on a moving Unmanned Ground Vehicle (UGV) has received extensive attention as a key technology. At present, this technology is confronted with such problems as operating in GPS-denied environments, a low accuracy of target location, the poor precision of the relative motion estimation, delayed control responses, slow processing speeds, and poor stability. To address these issues, we present a hybrid camera array-based autonomous landing UAV that can land on a moving UGV in a GPS-denied environment...

Recognizing human actions from varied views is challenging due to huge appearance variations in different views. The key to this problem is to learn discriminant view-invariant representations generalizing well across views. In this paper, we address this problem by learning view-invariant representations hierarchically using a novel method, referred to as Joint Sparse Representation and Distribution Adaptation (JSRDA).. . .

Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems. . .

With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. . .

An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle detection tasks. Therefore, how to make full use of them—especially in ground vehicle detection based on aerial imagery–has aroused wide academic concern. . .

Infrared human action recognition has many advantages, i.e., it is insensitive to illumination change, appearance variability, and shadows. Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video. . .

As the two most commonly used imaging devices, infrared sensor and visible sensor play a vital and essential role in the field of heterogeneous image matching. Therefore, visible-infrared image matching which aims to search images across them has important application and theoretical significance. However, due to the vastly different imaging principles, how to accurately match between visible and infrared image remains a challenge. . .

This work expands upon state-of-the-art multiscale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixelwise classification stage is incorporated in the CT-based tracker to obtain a relatively stable appearance model, by distinguishing object pixels from the background. .

Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. .

Cross-domain image matching, which investigates the problem of searching images across different visual domains such as photo, sketch or painting, has attracted intensive attention in computer vision due to its widespread application. Unlike intra-domain matching, cross-domain images appear quite different in various characteristics.

Robust extraction of consensus sets from noisy data is a fundamental problem in robot vision. Existing multimodel estimation algorithms have shown success on large consensus sets estimations. One remaining challenge is to extract small consensus sets in cluttered multimodel data set. In this article, we present an effective multimodel extraction method to solve this challenge.

Occlusion poses as a critical challenge in computer vision for a long time. Recently, the technique of synthetic aperture photography using a camera array has been regarded as a promising way to address the problem of occluded object imaging. Unfortunately, the expensive cost of a standard camera array system with the required calibration procedure still limits the widespread popularity of this technique.

The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs) is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences.