Camera Installation Research Articles

Development and application of a risk analysis methodology for road traffic accidents

Collision, rollover, swerving, and vehicle fire are road traffic accidents (RTAs) caused by driver, vehicle, and environment-related factors, resulting in diverse severity levels, such as property damages, injuries, and fatality. In addition, vagueness in detectability, e.g., close circuit television and driver admittance, culminates RTAs' risk analysis into a multifaceted problem. The present study performed root cause analysis to investigate RTAs, where the fishbone diagram categorized RTAs (e.g., collision and swerving) and identified primary (e.g., driver and environment) and secondary (e.g., overspeeding and tire burst) root causes. For 1158 km of intercity highways in Qassim (Saudi Arabia), fuzzy-failure mode and effect analysis evaluated 171 failure modes using 3500 RTAs' recorded over three years and knowledge of practice and experts. The study highlights collision, driver-related issues, and property damage as the leading category, root cause, and consequence. Comparing the baseline with the first realistic mitigation (speed camera installation) acknowledged a 53 % reduction in risk probability number. Automated driver assistance systems, rumble strips, variable speed limits, and increasing penalties for traffic violations hypothetically dropped 76 % of cumulative risk. The methodology handles limited data and subjective risk factors to help decision-makers minimize RTAs in Saudi Arabia and other jurisdictions.

Robust Estimation of Crowd Density Using Vision Transformers

Estimating and predicting crowd density at large events or during disasters is of paramount importance for enhancing emergency management systems. This includes planning effective evacuation routes, optimizing rescue operations, and ensuring efficient deployment of emergency services. Traditionally, surveillance systems that rely on cameras have been employed to monitor crowd movements. However, accurately estimating crowd density using such systems presents several challenges. These challenges stem primarily from the interaction between large crowds and the limitations of two-dimensional cameras in capturing the full scope of three-dimensional spaces. Optical distortions, environmental factors, and variations in camera angles further complicate the task, making accurate estimations difficult to achieve. To address these challenges, this paper introduces a robust method for calculating crowd density that leverages advanced vision transformers. By combining the output of these transformers with a two-stage neural network, the method effectively mitigates the limitations of traditional approaches. One of the key advantages of the proposed system is its robustness, which allows it to perform well across different camera specifications, installation locations, and image aspect ratios. The method applies and evaluates various deep learning techniques, introducing improvements to existing network structures that are better suited for the problem at hand. Extensive experimental verification demonstrates that the proposed method consistently produces accurate crowd density estimates, even in diverse and complex crowd environments. This robust performance underscores its potential for improving emergency management and crowd control in real-world situations.

One-Shot Any-Scene Crowd Counting With Local-to-Global Guidance.

Due to different installation angles, heights, and positions of the camera installation in real-world scenes, it is difficult for crowd counting models to work in unseen surveillance scenes. In this paper, we are interested in accurate crowd counting based on the data collected by any surveillance camera, that is to count the crowd from any scene given only one annotated image from that scene. To this end, we firstly pose crowd counting as a one-shot learning task. Through the metric-learning, we propose a simple yet effective method that firstly estimates crowd characteristics and then transfers them to guide the model to count the crowd. Specifically, to fully capture these crowd characteristics of the target scene, we devise the Multi-Prototype Learner to learn the prototypes of foreground and density from the limited support image using the Expectation-Maximization algorithm. To learn the adaptation capability for any unseen scene, estimated multi prototypes are proposed to guide the crowd counting of query images in a local-to-global way. CNN is utilized to activate the local features. And transformer is introduced to correlate global features. Extensive experiments on three surveillance datasets suggest that our method outperforms the SOTA methods in the few-shot crowd counting.

Leveraging BIM for Enhanced Camera Allocation Planning at Construction Job Sites: A Voxel-Based Site Coverage and Overlapping Analysis

In the construction industry, the imperative for visual surveillance mechanisms is underscored by the need for safety monitoring, resources, and progress tracking, especially with the adoption of vision intelligence technology. Traditional camera installation plans often move toward coverage and cost objectives without considering substantial coverage overlap, inflating processing and storage requirements, and complicating subsequent analyses. To address these issues, this research proposes a voxel-based site coverage and overlapping analysis for camera allocation planning in parametric BIM environments, called the PBA approach. The first step is to collect information from the BIM model, which is the input for the parametric modeling step. After that, the PBA approach simulates the virtual devices and the construction layout by employing visual language programming and then generates a coverage area. Lastly, the performance simulation and evaluation of various placement scenarios against predefined criteria are conducted, including visual coverage and overlapping optimization for eliminating data redundancy purposes. The proposed approach is evaluated through its application to construction projects. The results from these various implementations indicate a marked decrease in data overlap and an overall enhancement in surveillance efficacy. This research contributes a novel, BIM-centric solution to visual information adoption in the construction industry, offering a scalable approach to optimize camera placement while mitigating overlapping areas.

GRACE-FO attitude determination: Star camera installation matrix calibration and incremental quaternion integrator

The satellite attitude error is transmitted to the gravity field through KBR antenna phase center correction and non-conservative force conversion, which affects the accuracy of gravity field inversion. Therefore, obtaining high-precision satellite attitude is one of the important research contents of gravity field inversion. Each satellite of GRACE-FO is equipped with three SCs and an IMU for satellite attitude measurement, and fusion of these two types of data can obtain high-precision satellite attitude. In this paper, we use kalman filter to fuse the original GRACE-FO 1A observation data, and take into account the calibration of the installation matrix of the SCs during the fusion process. Meanwhile, a quaternion integrator that takes angle increments as inputs is derived from the equivalent rotation vector differential equation to address the computational inefficiency of first-order quaternion integrator, the accuracy and computational efficiency are verified using measured data. The experimental results show that after the calibration of the installation matrix of the SCs, the system bias of 20′′ caused by switching from three SCs observations to two SCs observations can be eliminated. The standard deviation of the attitude difference between the obtained attitude in this paper and the attitude calculated by JPL is at most 6′′ and the proposed incremental quaternion integrator has the same accuracy as the first-order quaternion integrator but with computational efficiency improved by at least 35%.

Evaluating the Effectiveness of Speed Cameras on Philadelphia’s Roosevelt Boulevard

This paper employs Bayesian negative binomial and Poisson models to examine the effectiveness of speed cameras at reducing crashes, injuries, and fatalities on and immediately around Roosevelt Boulevard, a particularly dangerous urban arterial in Philadelphia, PA. Camera installation also coincided with rapidly changing traffic safety outcomes throughout the summer after COVID-19 lockdowns and protests against police violence. To find the most appropriate control segments for our analysis, we use k-means clustering to identify the five most similar roadways. Compared with these roadways and other geographic controls, the treated sections of Roosevelt Boulevard experienced decreased crashes, injuries, and fatalities after camera installation. These decreases are on the high end of what is found in the academic literature on speed cameras. The findings for total crashes, traffic injuries, fatalities, and pedestrian injuries are statistically different from zero with a high degree of confidence. Differences in pedestrian fatalities are statistically different from zero with 90% confidence. Based on our findings, we recommend that the Commonwealth of Pennsylvania and City of Philadelphia extend and expand the automated speed enforcement pilot. Estimates of the economic benefits of reduced crashes substantially exceed the total fines paid by violators. Despite this positive safety effect, additional treatments will be required to make Roosevelt Boulevard substantially safer. The roadway still accounts for around 8% of all traffic fatalities in Philadelphia.

Fair Detour Domination in the Corona of Two Graphs: Optimizing CCTV Camera Installation for Efficient Surveillance

The objective of our research is to analyze the properties of FDD sets in the corona of two graphs and explore their practical application in CCTV camera installation. A set F ⊆ V(G) is considered to be a Fair Detour Dominating set (FDD-set) if it is detour dominating and the number of neighbors within set F is the same for any pair of vertices outside of F. Among these FDD sets, the fγd-set refers to the FDD-set with the smallest number of vertices, and its order defines the Fair Detour Domination number (fγd (G)). We have established that for any arbitrary graphs G1 and G2, fγd (G1 o G2) = |V(G1) iff fd(G2) = 1 we have determined the fγd number of corona products of any connected graph G with the path graph as well as the cycle graph. We also characterized FDD sets in the corona product of two connected graphs and provided a thorough description of how FDD sets can be used in optimizing CCTV camera installation for efficient surveillance.

Effect Analysis of Public Data-Based Automatic Traffic Enforcement Camera Installation Using the Comparison Group Method

Effect Analysis of Public Data-Based Automatic Traffic Enforcement Camera Installation Using the Comparison Group Method

Method for optimization of camera installation parameters for video monitoring of arbitrary surveillance zone

Method for optimization of camera installation parameters for video monitoring of arbitrary surveillance zone

Short-term effects of camera trap installation on incubation constancy in cranes

Research regarding how birds respond to the installation of cameras at nest sites is limited. We installed cameras at nests of Sandhill Cranes Antigone canadensis ( Linnaeus, 1758 ) and federally endangered Whooping Cranes Grus americana Linnaeus, 1758 in Juneau County, WI, as part of an ongoing study monitoring the reproductive success of Whooping Cranes in WI. The eastern population of Sandhill Cranes has grown, while Whooping Crane population growth has been slow, prompting the need to monitor the reproductive success of cranes on the refuge. We recorded the flight initiation distance during camera installation, the return time after camera installation, as well as measured the distance at which cameras were placed from each nest. We included temperature at camera deployment, age of nest, mode of access, ordinal date, and year in statistical regression models. We found an apparent difference in the observed flight initiation distances between the two species. Sandhill Cranes allowed researchers to approach their nests closer than Whooping Cranes prior to flushing. The post-disturbance return time was influenced by how far away the cameras were placed from the nest and the ambient temperature during camera deploy. Our study may help inform decisions regarding species response to methods and distance when deploying cameras, especially for endangered or disturbance-sensitive species.

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition.

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen's center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8-3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.

Modeling and Compensation of Positioning Error in Micromanipulation

In order to improve the positioning accuracy of the micromanipulation system, a comprehensive error model is first established to take into account the microscope nonlinear imaging distortion, camera installation error, and the mechanical displacement error of the motorized stage. A novel error compensation method is then proposed with distortion compensation coefficients obtained by the Levenberg–Marquardt optimization algorithm combined with the deduced nonlinear imaging model. The compensation coefficients for camera installation error and mechanical displacement error are derived from the rigid-body translation technique and image stitching algorithm. To validate the error compensation model, single shot and cumulative error tests were designed. The experimental results show that after the error compensation, the displacement errors were controlled within 0.25 μm when moving in a single direction and within 0.02 μm per 1000 μm when moving in multiple directions.

Whether and how congested is a road? Indices, updating strategy and a vision‐based detection framework

AbstractTraffic congestion detection method based on surveillance video is gradually widely used in intelligent transportation systems (ITS). Due to complex challenges such as weather change, vehicle occlusion, camera jitter, camera installation location, and so on, current methods are difficult to balance in real time and accuracy. Here, a new real‐time and robust traffic congestion detection framework and a vision‐based multi‐dimensional congestion detection model are proposed. Firstly, the framework introduces an object detector based on the lightweight convolutional neural network (CNN) and an efficient multi‐object IoU‐like tracker to obtain traffic dynamic information in real time. Then, traffic density, traffic velocity, and duration of instantaneous congestion are defined and a multi‐dimensional congestion detection model is established. Furthermore, an adaptive updating strategy of dynamic parameters is investigated. Finally, in multiple groups of comparative experiments, the framework is verified to be applicable to a variety of lightweight CNN detectors and IoU‐like trackers. The precision and recall of the multi‐dimensional congestion model can reach 95.1% and 92.1% respectively, with 43FPS. The comparative experimental results show that the proposed method is real‐time, more robust and accurate, and can be employed for online traffic congestion detection based on surveillance video.

Pre-set estimation-based in-silico silhouette-based methodology for improving the robustness to viewing direction difference for assisting forensic gait analysis.

Forensic gait analysis is used to visually and quantitatively analyze information regarding the appearance and style of walking that can be presented as evidence in the court. The demand for analyzing CCTV pedestrian footage in video surveillance has been increasing. The dependence of the accuracy of semiautomatic silhouette-based analysis, often used in forensic science, on the differences in the viewing directions is a very challenging issue that is yet to be resolved for real case applications. Currently, the different viewing directions used in comparison footage significantly decrease the accuracy of same person analysis when using the silhouette-based method, often used in the Japanese forensic science domain. A calibration-based method was previously prosed to resolve this problem, but it requires performing an elaborate measurement procedure at the camera installation site for an accurate analysis. In this study, we propose a novel in-silico silhouette-based analysis method that significantly expands the number of viewing direction pre-set settings to 900 from the 24 used in the previous method. Several software tools have been developed to ensure that all the procedures can be executed on a computer. The experimental results confirm that the accuracy of the proposed method is comparable to that of the calibration-based method. Furthermore, the practical comparison results from actual consultation confirmed the effectiveness of the proposed method under existing viewing direction differences. We therefore anticipate that the proposed method will be beneficial for improving the analysis accuracy in real cases and therefore serve as a substitute of the previous method.

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection.

Indoor positioning applications are developing at a rapid pace; active visual positioning is one method that is applicable to mobile platforms. Other methods include Wi-Fi, CSI, and PDR approaches; however, their positioning accuracy usually cannot achieve the positioning performance of the active visual method. Active visual users, however, must take a photo to obtain location information, raising confidentiality and privacy issues. To address these concerns, we propose a solution for passive visual positioning based on pedestrian detection and projection transformation. This method consists of three steps: pretreatment, pedestrian detection, and pose estimation. Pretreatment includes camera calibration and camera installation. In pedestrian detection, features are extracted by deep convolutional neural networks using neighboring frame detection results and the map information as the region of interest attention model (RIAM). Pose estimation computes accurate localization results through projection transformation (PT). This system relies on security cameras installed in non-private areas so that pedestrians do not have to take photos. Experiments were conducted in a hall about 100 square meters in size, with 41 test-points for the localization experiment. The results show that the positioning error was 0.48 m (RMSE) and the 90% error was 0.73 m. Therefore, the proposed passive visual method delivers high positioning performance.

Reliable Data Collection Methodology for Face Recognition in Preschool Children.

Most face datasets target adults who can make their own decisions. In the case of children, consent from parents or guardians is necessary to collect biometric information, thus making it very difficult. As a result, the amount of data on children is quite small and inevitably private. In this work, we built a database by collecting face data of 74 children aged 2–7 years in daycare facilities. In addition, we conducted an experiment to determine the best location to perform face recognition on children by installing cameras in various locations. This study presents the points and methods to be considered to build a children’s face dataset and also studies the optimal camera installation setups for the face recognition of children.

Generative planning for construction safety surveillance camera installation in 4D BIM environment

Surveillance cameras have become increasingly significant in the field of construction safety monitoring. With visual information, these cameras support safety managers to identify the potential hazards and deliver prompt feedback. However, developing the surveillance camera plan remains challenging in considering the workspace of working at height activities and the dynamic nature of the construction site. This study proposes a camera installation plan considering different site layouts with time during the optimization to resolve this problem. An approach for Generating Planning for Construction Safety Surveillance Camera Installation in 4D BIM Environment, called SCI4D. The SCI4D comprises three modules: The site profile module (SPM), which extracts the spatial and temporal information from a 4D BIM; Parametric modeling module (PMM), which simulate cameras and calculate the performance of surveillance systems, Installation planning module (IPM) suggests the appropriate solutions that meet the requirement. The efficiency of the SCI4D approach is validated through the test of a building project of 3000 square meters. SCI4D determined 45 solutions with optimal coverage within budget, $2500. The contribution of the proposed approach is to proactively aid project teams in preparing surveillance plan that ultimately supports construction safety monitoring using vision intelligence.

Less Temptation To Exceed The Speed Limit Or Towards Vision Zero

In 2020 the Lithuanian Government approved traffic safety programme Vision Zero. One of the integral measures applied to improve traffic safety and to reduce the number of road traffic infringements is the development of average speed cameras’ network on state significance roads. It is planned that the network of average speed cameras will cover more than 800 km of state significance road network in Lithuania in 2020-2021. Initially, it was planned to implement these measures only on rural roads. However, taking into consideration the principles of road eligibility for average speed camera installation, some road sections crossing the so-called linear settlements were selected to test the impact of such systems on driving habits as well. It is presumed that from the beginning of exploitation of these systems the reduction in the consequences of severe traffic accidents on the selected most dangerous state significance road sections will be observed.

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot.

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.