Camera Position Research Articles

Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts).In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy.ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods.Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.

A preliminary study of multispectral Cherenkov imaging and a Fricke-xylenol orange gel film (MCIFF) for online, absolute dose measurement.

Cherenkov luminescence imaging has shown potential for relative dose distribution and field verification in radiation therapy. However, to date, limited research utilizing Cherenkov luminescence for absolute dose calibration has been conducted owing to uncertainties arising from camera positioning and tissue surface optical properties. This paper introduces a novel approach to multispectral Cherenkov luminescence imaging combined with Fricke-xylenol orange gel (FXG) film, termed MCIFF, which can enable online full-field absolute dose measurement. By integrating these two approaches, MCIFF allows for calibration of the ratio between two spectral intensities with absorbed dose, thereby enabling absolute dose measurement. All experiments are conducted on a Varian Clinac 23EX, utilizing an electron multiplying charge-coupled device (EMCCD) camera and a two-way image splitter for simultaneous capture of two-spectral Cherenkov imaging. In the first part of this study, the absorbance curves of the prepared FXG film, which receives different doses, are measured using a fluorescence spectrophotometer to verify the correlation between absorbance and dose. In the second part, the FXG film is positioned directly under the radiation beam to corroborate the dose measurement capacity of MCIFF across various beams. In the third part, the feasibility of MCIFF is tested in actual radiotherapy settings via a humanoid model, demonstrating its versatility with various radiotherapy materials. The results of this study indicate that the logarithmic ratios of spectral intensities at wavelengths of 550±50 and 700±100nm accurately reflect variations in radiation dose (R2>0.96) across different radiation beams, particle energies, and dose rates. The slopes of the fitting lines remain consistent under varying beam conditions, with discrepancies of less than 8%. The optical profiles obtained using the MCIFF exhibit a satisfactory level of agreement with the measured results derived from the treatment planning system (TPS) and EBT3 films. Specifically, for photon beams, the lateral distances between the 80% and 20% isodose lines, referred to as the penumbra (P80-20) values, obtained through TPS, EBT3 films, and MCIFF, are determined as 0.537, 0.664, and 0.848cm, respectively. Similarly, for electron beams, the P80-20 values obtained through TPS, EBT3 films, and MCIFF are found to be 0.432, 0.561, and 0.634cm, respectively. Furthermore, imaging of the anthropomorphic phantom demonstrates the practical application of MCIFF in real radiotherapy environments. By combining an FXG film with Cherenkov luminescence imaging, MCIFF can calibrate Cherenkov luminescence to absorbed dose, filling the gap in online 2D absolute dose measurement methods in clinical practice, and providing a new direction for the clinical application of optical imaging to radiation therapy.

An Image Processing Approach for Real-Time Safety Assessment of Autonomous Drone Delivery

The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming via a single onboard camera and Global Positioning System (GPS) information. A two-stage processing procedure is proposed based on image segmentation and classification. Relevant parameters such as camera parameters, light parameters, dropping zone dimensions, and drone height from the ground are taken into account in the classification. The experimental results indicate that the proposed approach provides a fast method with reliable accuracy based on low-order calculations.

Korean Cattle 3D Reconstruction from Multi-View 3D-Camera System in Real Environment.

The rapid evolution of 3D technology in recent years has brought about significant change in the field of agriculture, including precision livestock management. From 3D geometry information, the weight and characteristics of body parts of Korean cattle can be analyzed to improve cow growth. In this paper, a system of cameras is built to synchronously capture 3D data and then reconstruct a 3D mesh representation. In general, to reconstruct non-rigid objects, a system of cameras is synchronized and calibrated, and then the data of each camera are transformed to global coordinates. However, when reconstructing cattle in a real environment, difficulties including fences and the vibration of cameras can lead to the failure of the process of reconstruction. A new scheme is proposed that automatically removes environmental fences and noise. An optimization method is proposed that interweaves camera pose updates, and the distances between the camera pose and the initial camera position are added as part of the objective function. The difference between the camera's point clouds to the mesh output is reduced from 7.5 mm to 5.5 mm. The experimental results showed that our scheme can automatically generate a high-quality mesh in a real environment. This scheme provides data that can be used for other research on Korean cattle.

Camera Path Generation for Triangular Mesh Using Toroidal Patches

Triangular mesh data structures are principal in computer graphics, serving as the foundation for many 3D models. To effectively utilize these 3D models across diverse industries, it is important to understand the model’s overall shape and geometric features thoroughly. In this work, we introduce a novel method for generating camera paths that emphasize the model’s local geometric characteristics. This method uses a toroidal patch-based spatial data structure, approximating the mesh’s faces within a predetermined tolerance ϵ, encapsulating their geometric intricacies. This facilitates the determination of the camera position and gaze path, ensuring the mesh’s key characteristics are captured. During the path construction, we create a bounding cylinder for the mesh, project the mesh’s faces and associated toroidal patches onto the cylinder’s lateral surface, and sequentially select grids of the cylinder containing the highest number of toroidal patches as we traverse the lateral surface. The centers of the selected grids are used as control points for a periodic B-spline curve, which serves as our foundational path. After initial curve generation, we generated camera position and gaze path from the curve by multiplying factors to ensure a uniform camera amplitude. We applied our method to ten triangular mesh models, demonstrating its effectiveness and adaptability across various mesh configurations.

SleepyWheels: An ensemble model for drowsiness detection leading to accident prevention

<p>Approximately 30% of traffic accidents in the world are attributed to driver drowsiness. Various approaches have been suggested by different research teams to detect drowsiness, but their methods have drawbacks. Some involve invasive techniques that cause driver discomfort, some involve too many false positives that cause distractions while driving while others rely on complex models that are too resource-intensive. In this paper, we present SleepyWheels, a novel drowsiness detection system. Our key insight is the combined use of two lightweight neural networks: a binary classifier and a facial landmark detector. This innovative approach minimizes false positives and proves resilient across diverse testing scenarios, such as different camera positions, variations in skin tone and when there is obscurement of facial features by objects like eyewear. Research outcomes include a working prototype of the system, a custom dataset for training the classifier and a trained model, which attains an impressive 97% accuracy rate. Deployment and testing were performed on Windows 10 but the system can be deployed on edge devices like Raspberry Pi. The lightweight nature of the models unlocks possibilities of deployment on mobile and embedded devices for use in vehicles.</p>

Evaluation of Photogrammetry System with Constraints on Camera Position and Attitude for Accurate Shape Measurement of Space Structures

Evaluation of Photogrammetry System with Constraints on Camera Position and Attitude for Accurate Shape Measurement of Space Structures

Camera Topology Graph Guided Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve vehicles across non-overlapping cameras. Most studies consider representation learning from single appearance information of the vehicle images. Some works adopt the spatio-temporal information to remove unreasonable vehicles to refine the results in the testing phase. However, they ignore the potential topological relations among cameras under the Closed Circuit Television (CCTV) camera systems in the training phase, which usually leads to suboptimal results due to the high intra-identity variations. To handle this problem, we propose a novel vehicle re-identification framework, which explicitly models the camera topological relations of all input images to aggregate neighbor images and thus acquires camera-independent representations. Specifically, we first construct a Camera Topology Graph (CTG) to elucidate the topological relations among cameras. It takes different cameras as nodes and constructs edges from four levels of the camera system, position, orientation, and individual. Then, we introduce a Camera Topology-based Graph Convolutional Network (CT-GCN), which suppresses irrelevant neighbor images and learns different camera representation functions. Finally, we propose a topological cross-entropy loss to obtain the more discriminative vehicle representations. The whole network is trained in an end-to-end manner. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed method against state-of-the-art vehicle Re-ID methods.

Three-Dimensional Leaf Edge Reconstruction Combining Two- and Three-Dimensional Approaches

Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.

Gender- Behind-The-Camera: Women’s Career Experiences in Television Stations in Lagos, Nigeria

This study examined the career experiences of women in behind the camera positions in television stations in Lagos State, Nigeria. Anchored on the Hegemony and Patriarchy theories, the study adopted survey, focus group discussion (FGD) and in-depth interview methods for data gathering. Samples for the survey, FGD and in-depth interview comprises 268 respondents, 6 participants each selected from the 6 sampled television stations and 2 participants each selected from the 6 sampled television stations in Lagos state respectively. The questionnaire, structured FGD guide and structured interview guide were used to generate the data used to answer the five research questions guiding the study. Results show that the respondents were more dominated in roles such as producer, reporter and editor, and women also dominate management roles in government owned television stations compared to the private owned television stations. Additionally respondents are perceived to be emotional (38.0%), sensitive (25.4%) and weak (23.9%). Results also shows that respondents are not promoted or appointed to managerial position in television stations based on gender, rather it is based on competence and efficiency. Results also shows that the major challenges facing respondents on their job are; late time closing hours, demands of home and work front, and the job been considered as too tedious/risky. The findings also revealed that these challenges will hinder respondents from attaining their career goals however the major prospect respondents have on the job is to get to the peak of their careers. Consequently, findings support tenets of Hegemony and Patriarchy theories. Keyword: Career experiences; behind the camera positions; television stations, challenges and prospects.

Sim2Real: Generative AI to Enhance Photorealism through Domain Transfer with GAN and Seven-Chanel-360°-Paired-Images Dataset.

This work aims at providing a solution to data scarcity by allowing end users to generate new images while carefully controlling building shapes and environments. While Generative Adversarial Networks (GANs) are the most common network type for image generation tasks, recent studies have only focused on RGB-to-RGB domain transfer tasks. This study utilises a state-of-the-art GAN network for domain transfer that effectively transforms a multi-channel image from a 3D scene into a photorealistic image. It relies on a custom dataset that pairs 360° images from a simulated domain with corresponding 360° street views. The simulated domain includes depth, segmentation map, and surface normal (stored in seven-channel images), while the target domain is composed of photos from Paris. Samples come in pairs thanks to careful virtual camera positioning. To enhance the simulated images into photorealistic views, the generator is designed to preserve semantic information throughout the layers. The study concludes with photorealistic-generated samples from the city of Paris, along with strategies to further refine model performance. The output samples are realistic enough to be used to train and improve future AI models.

Camera-aware re-identification feature for multi-target multi-camera tracking

Multi-target multi-camera tracking is an important research topic in intelligent surveillance to achieve re-identification (Re-ID) of moving targets across different cameras. However, Re-ID faces significant challenges owing to variations in cameras/viewpoints, making it difficult to learn discriminative feature representations for targets captured from different cameras/viewpoints. A new pipeline is introduced by balancing the across-cameras sample feature space in camera-aware Re-ID framework. Specifically, different sampling strategies play a crucial role on the performance under the same baseline and identify the feature discrepancy between cameras/viewpoints as a crucial factor. This proposed sampling strategy is called camera equalization sampling to learn enhanced feature disparities, which balances cameras under identity rather than randomly sampling in a batch. The sampled images are combined with non visual cues(cameras position encoding) to reduce intra class variance. For the mechanism of camera equalization sampling, the improved camera centric loss function can better reduce the negative impact of individual samples and provide stable learnable features.Our proposed method consists of three modules. (i) Camera equalization (CE) ensures that each batch collects at least one image from each camera for every identity, thereby enabling robust features. (ii) Camera position embedding (CPE) mitigates the scene bias caused by different cameras/viewpoints by encoding camera indices. (iii) Camera Center triplet loss(CCL) based on CE improves higher robustness to outliers and noisy labels. We demonstrated the proposed method's effectiveness on popular datasets such as DukeMTMC-reID, Market-1501, and MSMT17, achieving state-of-the-art performance.

SASFF: A Video Synthesis Algorithm for Unstructured Array Cameras Based on Symmetric Auto-Encoding and Scale Feature Fusion.

For the synthesis of ultra-large scene and ultra-high resolution videos, in order to obtain high-quality large-scene videos, high-quality video stitching and fusion are achieved through multi-scale unstructured array cameras. This paper proposes a network model image feature point extraction algorithm based on symmetric auto-encoding and scale feature fusion. By using the principle of symmetric auto-encoding, the hierarchical restoration of image feature location information is incorporated into the corresponding scale feature, along with deep separable convolution image feature extraction, which not only improves the performance of feature point detection but also significantly reduces the computational complexity of the network model. Based on the calculated high-precision feature point pairing information, a new image localization method is proposed based on area ratio and homography matrix scaling, which improves the speed and accuracy of the array camera image scale alignment and positioning, realizes high-definition perception of local details in large scenes, and obtains clearer synthesis effects of large scenes and high-quality stitched images. The experimental results show that the feature point extraction algorithm proposed in this paper has been experimentally compared with four typical algorithms using the HPatches dataset. The performance of feature point detection has been improved by an average of 4.9%, the performance of homography estimation has been improved by an average of 2.5%, the amount of computation has been reduced by 18%, the number of network model parameters has been reduced by 47%, and the synthesis of billion-pixel videos has been achieved, demonstrating practicality and robustness.

Impact of standardising indocyanine green fluorescence angiography technique for visual and quantitative interpretation on interuser variability in colorectal surgery.

Intra-operative colonic perfusion assessment via indocyanine green fluorescence angiography (ICGFA) aims to address malperfusion-related anastomotic complications; however, its interpretation suffers interuser variability (IUV), especially early in ICGFA experience. This work assesses the impact of a protocol developed for both operator-based judgement and computational development on interpretation consistency, focusing on senior surgeons yet to start using ICGFA. Experienced and junior gastrointestinal surgeons were invited to complete an ICGFA-experience questionnaire. They subsequently interpreted nine operative ICGFA videos regarding perfusion sufficiency of a surgically prepared distal colon during laparoscopic anterior resection by indicating their preferred site of proximal transection using an online annotation platform (mindstamp.com). Six ICGFA videos had been prepared with a clinical standardisation protocol controlling camera and patient positioning of which three each had monochrome near infrared (NIR) and overlay display. Three others were non-standardised controls with synchronous NIR and overlay picture-in-picture display. Differences in transection level between different cohorts were assessed for intraclass correlation coefficient (ICC) via ImageJ and IBM SPSS. 58 clinicians (12 ICGFA experts, 46 ICGFA inexperienced of whom 23 were either finished or within one year of finishing training and 23 were junior trainees) participated as per power calculations. 63% felt that ICGFA should be routinely deployed with 57% believing interpretative competence requires 11-50 cases. Transection level concordance was generally good (ICC = 0.869) across all videos and levels of expertise (0.833-0.915). However, poor agreement was evident with the standardised protocol videos for overlay presentation (0.208-0.345). Similarly, poor agreement was seen for the monochrome display (0.392-0.517), except for those who were trained but ICG inexperienced (0.877) although even here agreement was less than with unstandardised videos (0.943). Colorectal ICGFA acquisition and display standardisation impacts IUV with this specific protocol tending to diminish surgeon interpretation consistency. ICGFA video recording for computational development may require dedicated protocols.

Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi-Analytic Hierarchy Process.

Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot's mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students' presentation skills.

Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms

Robust and precise visual localization over extended periods of time poses a formidable challenge in the current domain of spatial vision. The primary difficulty lies in effectively addressing significant variations in appearance caused by seasonal changes (summer, winter, spring, autumn) and diverse lighting conditions (dawn, day, sunset, night). With the rapid development of related technologies, more and more relevant datasets have emerged, which has also promoted the progress of 6-DOF visual localization in both directions of autonomous vehicles and handheld devices.This manuscript endeavors to rectify the existing limitations of the current public benchmark for long-term visual localization, especially in the part on the autonomous vehicle challenge. Taking into account that autonomous vehicle datasets are primarily captured by multi-camera rigs with fixed extrinsic camera calibration and consist of serialized image sequences, we present several proposed modifications designed to enhance the rationality and comprehensiveness of the evaluation algorithm. We advocate for standardized preprocessing procedures to minimize the possibility of human intervention influencing evaluation results. These procedures involve aligning the positions of multiple cameras on the vehicle with a predetermined canonical reference system, replacing the individual camera positions with uniform vehicle poses, and incorporating sequence information to compensate for any failed localized poses. These steps are crucial in ensuring a just and accurate evaluation of algorithmic performance. Lastly, we introduce a novel indicator to resolve potential ties in the Schulze ranking among submitted methods. The inadequacies highlighted in this study are substantiated through simulations and actual experiments, which unequivocally demonstrate the necessity and effectiveness of our proposed amendments.

An integrated workflow for 2D and 3D posture analysis during vestibular system testing in mice.

Posture extraction from videos is fundamental to many real-world applications, including health screenings. In this study, we extend the utility and specificity of a well-established protocol, the balance beam, for examining balance and active motor coordination in adult mice of both sexes. The primary objective of this study is to design a workflow for analyzing the postures of mice walking on a balance beam. We developed new tools and scripts based on the FluoRender architecture, which can interact with DeepLabCut (DLC) through Python code. Notably, twenty input videos were divided into four feature point groups (head, body, tail, and feet), based on camera positions relative to the balance beam (left and right), and viewing angles (90° and 45° from the beam). We determined key feature points on the mouse to track posture in a still video frame. We extracted a standard walk cycle (SWC) by focusing on foot movements, which were computed by a weighted average of the extracted walk cycles. The correlation of each walk cycle to the SWC was used as the weight. We learned that positions of the camera angles significantly improved the performance of 2D pose estimation (90°) and 3D (45°). Comparing the SWCs from age-matched mice, we found a consistent pattern of supporting feet on the beam. Two feet were consistently on the beam followed by three feet and another three feet in a 2-3-3 pattern. However, this pattern can be mirrored among individual subjects. A subtle phase shift of foot movement was also observed from the SWCs. Furthermore, we compared the SWCs with speed values to reveal anomalies in mouse walk postures. Some anomalies can be explained as the start or finish of the traversal, while others may be correlated to the distractions of the test environment, which will need further investigation. Our posture analysis workflow improves the classical behavioral testing and analysis, allowing the detection of subtle, but significant differences in vestibular function and motor coordination.

Poster Session I: Perspective-correct rendering for active observers.

Stereoscopic, head-tracked display systems can show users realistic, world-locked virtual objects and environments (i.e., rendering perspective-correct binocular images with accurate motion parallax). However, discrepancies between the rendering pipeline and physical viewing conditions can lead to perceived instability in the rendered content resulting in reduced immersion and, potentially, visually-induced motion sickness. Precise requirements to achieve perceptually stable world-locked rendering (WLR) are unknown due to the challenge of constructing a wide field of view, distortion-free display with highly accurate head and eyetracking. We present a custom-built system capable of rendering virtual objects over real-world references without perceivable drift under such constraints. This platform is used to study acceptable errors in render camera position for WLR in augmented and virtual reality scenarios, where we find an order of magnitude difference in perceptual sensitivity. We conclude with an analytic model which examines changes to apparent depth and visual direction in response to camera displacement errors, and visual direction is highlighted as a potentially important consideration for WLR alongside depth errors from incorrect disparity.

Modelling of Interactive Experience and User Satisfaction in Interior Design Based on Virtual Reality Technology

Abstract This paper firstly analyzes the essential features of virtual reality technology, constructs the framework of the interior design system based on virtual reality technology, and designs the interaction content of the system as well as the interaction process architecture. Secondly, the interior design system is technically analyzed, and the system’s moving trajectory relative to the environment is determined by using specific point calculation and camera position estimation algorithms, and specific target detection algorithms extract specific target HOC features. Finally, the platform is tested for performance, and the data is quantitatively analyzed using relevant data collected from users in the system as a research sample. The results show that 43 users are satisfied with being able to visit freely in a continuous space, 45 users are all very satisfied with switching the elements in the scene, and they think that replacing the items in the scene is conducive to participating in the design of the program and selecting the decorations they are satisfied with. It shows that the interior design system based on VR technology can directly interact with virtual information in real time, which improves the user’s interactive experience and makes the design “human-oriented”, starting from human needs and feelings.

Recommendations on Use of DIC in Experimental Geotechnics

Abstract Image-based deformation measurement techniques, like digital image correlation (DIC), are used to measure the displacement of soil particles and thereby to investigate the behavior of geo-structure in experimental geotechnics. The precision of the measurement from the DIC technique depends on the position of the camera and light panels, the color temperature of light, size of the region of interest (RoI), type and relative density of sand, seeding ratio, type of camera, and strain rate used in experiments. In the paper, the effect of above-mentioned parameters is investigated experimentally in 1-g model tests. The studies show that the precision in DIC measurements is dependent on the position of camera and light panels, size of RoI, texture of the sand, and seeding ratio. However, the relative density of sand and strain rate do not significantly affect the results from the DIC analysis. The efficacy in the measurement of the large deformations and suitability of smartphone cameras using DIC are also addressed. Last, we developed recommendations to obtain precise results from DIC in experimental geotechnics.