Camera Selection Research Articles

Investigating Human-Robot Interactions with a Quadruped Robot

Robots are becoming more prevalent in our daily lives. As robots become more common in society, we must understand how people react and interact with them. This study leverages Boston Dynamics' Spot robot to examine human responses to quadruped robots. Previous studies had Spot walking alongside participants at fixed speeds, independent of their gait. Improving upon this method, I developed a program that enables Spot to detect and follow fiducial markers (AprilTags). Using the Robot Operating System (ROS), the depth and position of the tag within the camera’s frame were extracted and translated into motion commands for the robot. The tag is attached to an apparatus on the hip that extends the tag near the subject’s knee, allowing Spot to track the participant’s trunk speed and maintain a consistent distance, offering a more adaptive walking experience. The program allows for variations in the acceleration, height, and camera selection (front/back, left/right) through a custom Graphical User Interface (GUI), allowing for diverse trial conditions. Extensive testing identified considerations to optimize the walking experience. Factors included gradual acceleration to allow Spot to react and maintain detections, and the set requirement to run only a single camera at a time for optimized processing. Additionally, keeping the tag unobstructed and square to the camera is critical to avoid failures in detection. Overall, Spot's new capabilities will assist in further studies of human and robot interactions.

Viewpoint Selection for 3D-Games with f-Divergences.

In this paper, we present a novel approach for the optimal camera selection in video games. The new approach explores the use of information theoretic metrics f-divergences, to measure the correlation between the objects as viewed in camera frustum and the ideal or target view. The f-divergences considered are the Kullback-Leibler divergence or relative entropy, the total variation and the χ2 divergence. Shannon entropy is also used for comparison purposes. The visibility is measured using the differential form factors from the camera to objects and is computed by casting rays with importance sampling Monte Carlo. Our method allows a very fast dynamic selection of the best viewpoints, which can take into account changes in the scene, in the ideal or target view, and in the objectives of the game. Our prototype is implemented in Unity engine, and our results show an efficient selection of the camera and an improved visual quality. The most discriminating results are obtained with the use of Kullback-Leibler divergence.

Revisiting Person Re-Identification by Camera Selection.

Person re-identification (Re-ID) is a fundamental task in visual surveillance. Given a query image of the target person, conventional Re-ID focuses on the pairwise similarities between the candidate images and the query. However, conventional Re-ID does not evaluate the consistency of the retrieval results of whether the most similar images ranked in each place contain the same person, which is risky in some applications such as missing out a place where the patient passed will hinder the epidemiological investigation. In this work, we investigate a more challenging task: consistently and successfully retrieving the target person in all camera views. We define the task as continuous person Re-ID and propose a corresponding evaluation metric termed overall Rank-K accuracy. Different from the conventional Re-ID, any incorrect retrieval under an individual camera view that raises an inconsistency will fail the continuous Re-ID. Consequently, the defective cameras, in which the images are hard to be automatically associated with the images from other views, strongly degrade the performance of continuous person Re-ID. Since the camera deployment is crucial for continuous tracking across camera views, we rethink person Re-ID from the perspective of camera deployment and assess the quality of a camera network by performing continuous Re-ID. Moreover, we propose to automatically detect the defective cameras that greatly hamper the continuous Re-ID. Because brute-force search is costly when the camera network becomes complicated, we explicitly model the visual relations as well as the spatial relations among cameras and develop a relational deep Q-network to select the properly deployed cameras and the un-selected cameras are regarded as the defective cameras. Since most existing datasets do not provide topology information about the camera network, they are unsuitable for investigating the importance of spatial relations on camera selection. Thus, we collect a new dataset including 20 cameras with topology information. Compared with randomly removing cameras, the experimental results show that our method can effectively detect the defective cameras so that people could take further operations on these cameras in practice (https://www.isee-ai.cn/∼yixing/MCCPD.html).

IoVT-based efficient solution for optimal active smart camera selection in a tracking mission

Nowadays, to ensure people’s and property’s safety, cities have dozens of cameras installed on almost every corner. However, the haphazard deployment of these cameras often results in overlapping fields of view, leading to redundant processing of the same events by multiple cameras. This situation requires more power, huge bandwidth, and more storage capacity for collecting, processing and distributing multimedia data over the network. To overcome these challenges, we propose a novel Internet of Video Things-based system that efficiently reduces the number of cameras involved in processing redundant events. The proposed system operates through a two-step process. Firstly, a leader is elected from a group of cameras based on the degree of overlap in their fields of view, serving as the coordinator. Secondly, the system intelligently selects suitable assistants from neighboring cameras to complement the leader’s vision when it becomes inadequate for effective object tracking. Consequently, only the leader and its assistants remain active, while other neighboring cameras remain in an inactive state. The evaluation demonstrates that our IoVT-based solution offers an efficient approach by minimizing redundancy, optimizing resource utilization and overcoming the challenges posed by the random installation of cameras in urban environments.

Automatic Camera Selection, Shot Size, and Video Editing in Theater Multi-Camera Recordings

Automatic Camera Selection, Shot Size, and Video Editing in Theater Multi-Camera Recordings

Criteria and model of choosing a surveillance equipment

Problem. The relevance of the work is to increase the efficiency of security in Ukraine based on the development of computer technology for the selection of surveillance equipment for the enterprise, which will improve the quality of camera selection and increase productivity in security sphere. Goal. The aim is development of computer technology for video surveillance systems, which will reduce the cost and time required to choose a video camera by developing mathematical models for choosing video cameras. The object of research is the process of selecting the elements of the video surveillance system. The subject of research is the model of selection of elements of the video surveillance system. Methodology. This work gives the analysis of the development of surveillance systems in Ukraine, theoretical material on video cameras, substantiates the criteria for the selection of cameras, develops mathematical model for the selection of surveillance equipment. Results. The developed criteria and mathematical model of an video camera selection allow making scientifically substantiated decisions on the acquisition of a camera by an enterprise according to many criteria. This will reduce the time and cost of making a decision. Originality. Further development of making decision methods was made according to many criteria and the mathematical model of the surveillance equipment selection was developed. Practical value. The proposed mathematical model will allow the development of computer technology for the selection of video cameras for the enterprise. This will reduce the time and money to choose surveillance equipment.

Don’t forget to look up: operating in the upper abdomen

Given the marked improvement in laparoscopic technology, gynecologic surgeons feel comfortable operating in the pelvis for a variety of gynecologic pathologies. When pathology is found outside of the pelvis, however, gynecologic surgeons find operating in the upper abdomen challenging. Operating in the upper abdomen is difficult given the loss of ergonomics and the impression of operating backwards. It is prudent for gynecologic surgeons to master operating in the upper abdomen given the variety of pathologies a gynecologist can encounter outside of the pelvis both benign such as endometriosis and adhesions, as well as malignant like staging procedures, omentectomy, and debulking. We aim to describe our operating room modifications that help simulate operating in the upper abdomen as if one was operating in the pelvic cavity. Strategies to improve efficiency and ergonomics when operating in the upper abdomen include: operating room set up, switching monitors to the patient's shoulders bilaterally, changing surgeon location to the right side of the patient, port-hopping, and 30 degree camera selection. We have also created an instructional video with the tools to improve surgeon confidence and ergonomics when operating in the upper abdomen.

Don't Forget to Look Up: Operating in the Upper Abdomen

<h3>Study Objective</h3> To illustrate strategies to improve efficiency and ergonomics when operating in the upper abdomen. <h3>Design</h3> A stepwise demonstration of our operating room modifications when operating in the upper abdomen with narrated video footage. <h3>Setting</h3> Given the marked improvement in laparoscopic technology, gynecologic surgeons feel comfortable operating in the pelvis for a variety of gynecologic pathologies. When pathology is found outside of the pelvis, however, gynecologic surgeons find operating in the upper abdomen challenging. Operating in the upper abdomen is difficult given the loss of ergonomics and the impression of operating backwards. It is prudent for minimally invasive gynecologic surgeons to master operating in the upper abdomen given the variety of pathologies a gynecologist can encounter outside of the pelvis. We aim to describe our operating room modifications that help simulate operating in the upper abdomen as if one was operating in the pelvic cavity. <h3>Patients or Participants</h3> A compilation of various patient procedures showcasing upper abdominal surgery including resection of pericardial endometriosis, Morrison's pouch endometriosis and a para-aortic lymphadenectomy. <h3>Interventions</h3> The patients elected to undergo laparoscopic surgery for their individualized evaluation and treatment plan. If the surgery is a combined procedure, after completion of the pelvic phase, the operating room must be modified to proceed with the upper abdomen. Strategies to improve efficiency and ergonomics when operating in the upper abdomen include: •Switching monitors to the patients' shoulders bilaterally •Changing surgeon location to the right side of the patient •Changing port configuration •30-degree camera selection <h3>Measurements and Main Results</h3> All patients recovered well without any postoperative complications. <h3>Conclusion</h3> Although gynecologic surgeons have the skills and adequate knowledge of anatomy to operate in the upper abdomen, some lack the ability to adapt their surgical set up to operate outside the pelvis. Gynecologists should familiarize themselves with strategies and techniques to be able to confidently operate in the upper abdomen.

Swimming Movement Guidance Training System Based on OpenCL Technology

In order to improve the physical quality and even ability of swimmers, this study built OpenCL technology for the swimming movement design system of swimmers, so as to design a series of movements. Combined with the capture system, this paper captures and processes the athletes’ movements, obtains the key position information, and calculates the accurate data. It can provide effective guidance for accurately positioning athletes’ swimming movements, enable users to have a clearer understanding of their own movements, improve users’ movements, and enhance the system operation effect. According to the technology and application requirements of virtual swimming training simulator, the overall design scheme of the device is determined. From the point of view of ensuring the comfort and safety of simulated virtual swimming, the design, installation, and strength verification of the supporting structure are carried out. Hardware design and selection shall be carried out according to the requirements of head position detection and virtual environment presentation, including the design of positioning ball set, camera selection, and VR head display selection. The software functions of head position detection program and virtual scene program are designed according to the requirements of natural human-computer interaction. The designed swimming guidance training system has complete functions and stable operation and can be used as an auxiliary tool for swimming training.

BIMBINGAN TEKNIS PEMASANGAN PERANGKAT SPC CAMERA MODEL KST2 UNTUK KEAMANAN WARGA PERUMAHAN CITRA CANDI MENDUT MALANG

At this time the demand for security cameras is increasing in line with the increasing number of criminal acts, this technology seems to be required to be installed in various public places and facilities that require more supervision in order to create security and safety. IP Camera (type KST2 SPC) is the latest solution in the field of security. This monitoring device has a monitor range that is not limited to the room, but the IP camera can also be accessed by a personal computer or laptop that is connected directly via LAN, Internet, or cellular network. IP Camera is a long-term solution that is very useful, very flexible, and can work for the next 10 years. IP Camera has other features such as camera selection and navigation features that affect video size and access speed. IP Camera is a CCTV (Closed Circuit Television) camera that uses Internet Protocol to transmit image data and control signals. Thus, IP cameras have several advantages over analog CCTV. In its use, this tool is also quite easy to integrate with various electronic-based early warning systems. Data from camera readings can be stored on a microSD which can store recording activity data for a full month.

Edge Intelligence for Rendering Green Camera-Network-as-a-Service

This work proposes the Green Camera-Network-as-a-Service (G-CNaaS) architecture, which provides on-demand camera networks to multiple end-users simultaneously while utilizing minimal energy. G-CNaaS simultaneously reduces the carbon footprint and eliminates the single application-centric approach of traditional camera networks (TCNs) by enabling each camera to participate in multiple Virtual-Camera-Networks (VCNs) and selecting an optimal set of cameras for each VCN. We couple each camera node in every VCNs with a learning model suitable for the requested application. We assign an intelligent edge device to each VCN to analyze time-sensitive events. We introduce the camera selection factor by leveraging the properties of cameras: 1) field-of-view (FoV); 2) angular-distance; 3) observation range; and 4) residual energy to select the optimal camera set. The results of the extensive simulation of the G-CNaaS architecture show that it excels in performance concerning attributes such as the average lifetime, fair distribution of the work among the camera owners, and cost-effectiveness compared to the TCNs. We observe that the expenditure of a user using the TCN varies by 88.7%, while in the case of G-CNaaS, the expenditure varies by 10.28% with the increase in time from 1–60 months. On the other hand, the average energy consumed increases by 59.88% and 99.5% in the presence of 10 and 20 camera sensor owners.

Evaluation of cameras and image distance for CNN-based weed detection in wild blueberry

Agricultural herbicide application efficiency can be improved using smart sprayers which provide site-specific, rather than broadcast, applications of agrochemicals. The YOLOv3-Tiny convolutional neural network (CNN) was trained to detect two weeds, hair fescue and sheep sorrel, in images captured from wild blueberry fields throughout Nova Scotia, Canada. An evaluation was performed in three commercial wild blueberry fields in Nova Scotia to examine the effects of camera selection and target distance on detection accuracy. A Canon T6 DSLR camera, an LG G6 smartphone, and a Logitech c920 webcam were used to capture RGB images at varying distances from target weeds. Mean F1-scores for each combination of camera and image height were analysed in a 3 × 3 factorial arrangement for hair fescue and a 3 × 2 factorial arrangement for sheep sorrel. Images captured from 0.98 m with the LG G6 and Canon T6 produced F1-scores of up to 0.97 for detection of at least one hair fescue tuft. Images captured with the LG G6 and Canon T6 DSLR from 0.57 m achieved F1-scores of 0.94 and 0.93, respectively, for detection of at least one sheep sorrel plant per image. Sheep sorrel was undetectable in images from the Logitech c920 under 19 of 27 parameter combinations. Future work will involve using the CNN to control herbicide applications with a real-time smart sprayer. Additionally, the CNN will be used in a web-based application to detect target weeds and provide site-specific information to aid management decisions. Using a CNN to detect weeds will create improvements in management techniques, resulting in cost-savings and greater sustainability for the wild blueberry industry.

Context-based camera selection from multiple video streams

Context-based camera selection from multiple video streams

Research on evaluation method of stereo vision measurement system based on parameter-driven

Stereo vision measurement system is widely used in aerospace manufacturing, intelligent robot and other fields because of its unique advantages. However, when using the stereo vision measurement system for measurement, there is no guidance for the parameter configuration of the measurement system for the scene and task constraints, resulting in the stereo vision measurement system cannot play the maximum performance. In this paper, from two aspects of local measurement of camera parameters and global measurement of system parameters, the relevant model is established to analyze the measurement performance of a single independent camera and the performance of the whole stereo vision measurement system. Firstly, the relationship between single camera calibration parameters and measurement accuracy is analyzed; secondly, the relationship between stereo measurement system parameters and measurement accuracy is analyzed; finally, the stereo vision measurement error function is established to guide the design of measurement system parameters. According to the actual measurement experiment, the correctness of the analysis and the accuracy of the established function are verified, it is efficiently used for guiding the parameter design and camera selection of the measurement system in different measurement scene and task.

Point‐Based Neural Rendering with Per‐View Optimization

AbstractThere has recently been great interest in neural rendering methods. Some approaches use 3D geometry reconstructed with Multi‐View Stereo (MVS) but cannot recover from the errors of this process, while others directly learn a volumetric neural representation, but suffer from expensive training and inference. We introduce a general approach that is initialized with MVS, but allows further optimization of scene properties in the space of input views, including depth and reprojected features, resulting in improved novel‐view synthesis. A key element of our approach is our new differentiable point‐based pipeline, based on bi‐directional Elliptical Weighted Average splatting, a probabilistic depth test and effective camera selection. We use these elements together in our neural renderer, that outperforms all previous methods both in quality and speed in almost all scenes we tested. Our pipeline can be applied to multi‐view harmonization and stylization in addition to novel‐view synthesis.

The effect of mobile camera selection on the capacity to predict water turbidity.

Recently, cameras of mobile have phones emerged as an alternative for quantifying water turbidity. Most of these studies lack a strategy to determine the water turbidity for new samples, focusing mainly on one particular device. Nevertheless, widespread use of these approaches requires a predictive capacity on out-of-the-sample images acquired in devices of different capabilities. We studied the influence of mobile device camera sensors on the predictive performance of water turbidity for non-previously observed turbid images. For this, a reference database with turbid images acquired for different mobile devices was constructed. A machine learning method based on image quality measures and linear classifiers (least squares and LASSO) was proposed to perform predictions on each mobile device. Relative accuracy and precision were evaluated. Results suggest that these approaches may provide accurate predictions reaching most than 80% of relative accuracy with high test-retest reliability (> 0.99). Nevertheless, our results also indicate that the predictive performance levels dropped in low capacity quality sensors. Therefore, despite the high performance that can be reached using these approaches, widespread use on multiple mobile devices may require further development of low-quality sensors and a better understanding of their operative ranges.

Reinforcement Learning Based Querying in Camera Networks for Efficient Target Tracking

Surveillance camera networks are a useful monitoring infrastructure that can be used for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on re-identification problems and trajectory association problems. However, as camera networks grow in size, the volume of data generated is humongous, and scalable processing of this data is imperative for deploying practical solutions. In this paper, we address the largely overlooked problem of scheduling cameras for processing by selecting one where the target is most likely to appear next. The inter-camera handover can then be performed on the selected cameras via re-identification or another target association technique. We model this scheduling problem using reinforcement learning and learn the camera selection policy using Q-learning. We do not assume the knowledge of the camera network topology but we observe that the resulting policy implicitly learns it. We evaluate our approach using NLPR MCT dataset, which is a real multi-camera multi-target tracking benchmark and show that the proposed policy substantially reduces the number of frames required to be processed at the cost of a small reduction in recall.

Development of Machine Vision to Increase the Level of Automation in Indonesia Electronic Component Industry

Human involvement in the assembly part manufacturing process is still relatively high. However, automation solutions are not flexible enough to be applied to manufacturing systems. It is essential to evaluate each work activity so that automation can be implemented effectively. We developed an automatic vision inspection using machine vision. The level of automation (LoA) in the company increases, and the impact caused by process failures on manual systems can be eliminated during inspection activities. The automation level increase in the inspection area is described and analyzed using the Hierarchy Task Analysis (HTA). Inspection data process activity and quality data are collected to determine the CCD camera selection, lamp selection, and lens selection. Three quality objectives, such as geometric quality, surface quality, and structural quality, are identified automatically using machine vision. Furthermore, after applying machine vision, an analysis of current LoA conditions and future LoA conditions is carried out. The results showed that the application of machine vision could increase the Level of Automation in the product inspection activity by 81.8%. There is a strong correlation (R = 0.924) between manual measurements carried out by operators and machine vision.

IMPLEMENTASI METODE ANALITYCAL HIERARCHY PROCESS (AHP) DAN TOPSIS UNTUK PEMILIHAN KAMERA

In today’s techonologically sophisticated ,camera are an unusual item number one amoung the millennial. Camera are the property of every millennial now, The camera mirrolless is itself used to take video and picture at an excellent resolution and the result will be presented by social media user. The user have option to consider according fit the requirement. The study is conducted to recommend prospective buyer in camera selection with a consideration of photographic ,sutter speed, video resolution, sensitivity and battery resistance. Web-based decision-making system using PHP and MySQL database, by using AHP method and TOPSIS. The AHP is an analytic method for solving flexible quantitative and qualitative. AHP has an advantage on the scale of the measurement given to the structure of the hierarchy, but the AHP method has a boundary in the number of comparison used that requires a TOPSIS method, then the approach of TOPSIS has a simple but regular and logical method of steps that form a system. The result of this camera study which is recommended from the approach, alternative selection has an indirect distance from the desired distance.

Camera Selection for Occlusion-Less Surgery Recording via Training With an Egocentric Camera

Recording surgery is an important technique for education and the evaluation of medical treatments. However, capturing targets such as the surgical field, surgical tools, and the surgeon’s hands, is almost impossible since these targets are heavily occluded by the surgeon’s head and body during a surgery. We used a recording system in which multiple cameras are installed in the surgical lump, supposing at least one camera would capture the target without occlusion. As this system records multiple video sequences, we address the task to select a best view camera automatically. Recently, learning-based approaches in a fully supervised manner have been proposed for this task, but these previous approaches completely rely on manual annotation of the training data. In this paper, we focus on the eye tracker mounted on the surgeon’s head, which can capture the recording targets without occlusion. Employing this first-person-view video synchronized with multiple videos of the surgical lump, we propose a novel camera selection approach using a self-supervised learning framework. In experiments, we created a dataset composed of four different breast surgery. Our extended experiments showed that our approach successfully switched to the best camera view without manual annotation and achieved competitive accuracy compared with conventional supervised methods. Also, our approach yielded effective visual representations comparable to state-of-the-art self-supervised learning frameworks.