Video Camera Research Articles

Visual Fall Detection Analysis Through Computer Vision and Deep Learning – Technology Proposition

Advances in modern medicine has increased humans’ life span. Orderly adults face mobility problems while aging. They feel less fit to continue any activity for short intervals too. This is due to declining fitness levels or muscle strength, diminished dexterity, and loss of balance. These symptoms lead to the fall of the individual and sometimes fatal too, if immediately not attended to. It’s an alarming issue for people staying alone. They may pose significant health risks and need immediate assistance. Fall detection technologies are majorly categorised as wearable sensors and ambient sensors. Fall detection wearable devices like pendant necklaces, watches and wristband devices, and clip-on medical alerts use accelerometers to detect rapid downward movements that can indicate a fall. They often also include manual alert buttons, for an increased accuracy. This requires technology comfort and awareness for usage. Ambient home sensors use video cameras to monitor the user’s movement and detect falls. When the fall is transmitted to a monitoring center, a representative typically will call the user to check on them before notifying contacts or calling for emergency services, but this can depend on the user’s preferences and risk factors. In this paper we propose a technology, using security cameras to record videos and create a video-based fall detection system. The system uses computer vision and deep learning algorithms to accurately recognize fall-related movements and distinguish them from regular activities. This system can be integrated to prompt alerts to emergency contacts, thus assisting in providing immediate aid to individuals who have experienced a fall. For higher accuracy, multiple-angle videos and multi-person tracking is integrated in this system to estimate the intensity of the fall for immediate attention. Thus, this fall detection system can contribute to the safety, well-being and independence of individuals at risk of falling.

Image moment-based visual positioning and robust tracking control of ultra-redundant manipulator

Image moment features can describe more general target patterns and have good decoupling properties. However, the image moment features that control the camera’s rotation motion around the x-axis and y-axis mainly depend on the target image itself. In this paper, the ultra-redundant manipulator visual positioning and robust tracking control method based on the image moments are advocated.First, six image moment features used to control camera motion around the x-axis and around the y-axis are proposed. And then, a novel method is proposed to use to select image features. For tracking a moving target, a kalman filter combined with adaptive fuzzy sliding mode control method is proposed to achieve tracking control of moving targets, which can estimate changes in image features caused by the target’s motion on-line and compensate for estimation errors. Finally, the experimental system based on Labview-RealTime system and ultra-redundant manipulator is used to verify the real-time performance and practicability of the algorithm. Experimental results are presented to illustrate the validity of the image features and tracking method.

Microsaccade-inspired event camera for robotics.

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements, the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call artificial microsaccade-enhanced event camera (AMI-EV). Benchmark comparisons validated the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrated the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks.

Associations between Stress Mindset and Changes in Positive Affect during a Speech Task: A Preliminary Study

A stress mindset is an individual’s belief that stress has either enhancing (stress-is-enhancing mindset: SEM) or debilitating (stress-is-debilitating mindset: SDM) consequences. This study examined the associations between SEM and changes in positive affect during a speech task using a sample of 32 Japanese college students as participants. It was hypothesized that participants with SEM would show a greater increase in positive affect over time than those with SDM. The participants rested for three minutes, prepared for three minutes, delivered a speech about themselves in front of an evaluator and video camera (task period), and again rested for three minutes. Positive affect was measured immediately after the two resting periods and during the speech. The participants were divided into groups of 17 and 15 in the SEM and SDM groups, respectively, based on their stress mindset scores. Both groups were fairly matched concerning age and male/female ratio. Change patterns of positive affect did not differ between the two groups, which did not support the hypothesis. However, positive affect was significantly higher in the SEM group compared with the SDM group. The present results differed from previous findings reported in North America. Inconsistent results are discussed related to differences in the speech task and cross-cultural variations of happiness and coping with stressors between North America and Japan.

Development of an ICT Laparoscopy System with Motion-Tracking Technology for Solo Laparoscopic Surgery: A Feasibility Study

The increasing demand for laparoscopic surgery due to its cosmetic benefits and rapid post-surgery recovery is juxtaposed with a shortage of surgical support staff. This juxtaposition highlights the necessity for improved camera management in laparoscopic procedures, encompassing positioning, zooming, and focusing. Our feasibility study introduces the information and communications technology (ICT) laparoscopy system designed to aid solo laparoscopic surgery. This system tracks a surgeon’s body motion using a controller, manipulating an embedded camera to focus on specific surgical areas. It comprises a camera module, a camera movement controller, and a motor within the main body, operating connected wires according to controller commands for camera movement. Surgeon movements are detected by an inertial measurement unit (IMU) sensor, facilitating precise camera control. Additional features include a foot pedal switch for motion tracking, a dedicated trocar for main body stability, and a display module. The system’s effectiveness was evaluated using an abdomen phantom model and animal experimentation with a porcine model. The camera responded to human movement within 100 ms, a delay that does not significantly affect procedural performance. The ICT laparoscopy system with advanced motion-tracking technology is a promising tool for solo laparoscopic surgery, potentially improving surgical outcomes and overcoming staff shortages.

Enhanced real-time road-vehicles’ detection and tracking for driving assistance

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.

Exploring morphometric transformations in temperature-sensitive NiTi instruments alongside real-time intracanal temperature dynamics.

This study was twofold: (i) it aimed to investigate the morphometric changes of three temperature-sensitive nickel-titanium (NiTi) instruments at different temperatures, and (ii) to conduct an invivo real-time analysis of intracanal temperature changes. Changes in the shape and length of XP-Endo Shaper, XP-Endo Finisher, and XP-Endo Finisher-R were evaluated in real time whilst heated in a temperature-controlled water bath from 22 to 45°C. Instruments were fixed to a laminated water-resistant 1 mm graph paper attached to a stone block. Instruments were imaged whilst subjected to increasing temperature using a digital camera attached to an operating microscope. From recorded videos, still frames were extracted at 10-s intervals and changes in the length and shape of each instrument were measured and changes were plotted against time. Moreover, the intracanal temperature of distal roots of lower molars was measured invivo for patients attending the clinic for non-surgical root canal treatments. The temperature was measured using a K-type thermocouple probe inserted into the mid-root level after irrigating the canal with a solution set at room temperature (22°C) or heated to 45°C. The intraoral and intracanal temperatures were recorded using a video camera for 180 s at 5-s intervals to plot the change in the intraoral and intracanal temperature, after both irrigation solution temperatures, with time. The shape transformation of XP-Endo Shaper began at 31.5 ± 2.0°C and reached its optimal transformation at 35.1 ± 1.0°C. For the Finisher and Finisher-R, shape transformations began at 29.2 ± 1.9 and 26.9 ± 2.2°C reaching the optimal transformation at 33.9 ± 1.4 and 32.7 ± 1.7°C, respectively. The average decreases in lengths of XP-Endo Shaper, Finisher, and Finisher-R after full transformation were 0.43 ± 0.23, 1.07 ± 0.22, and 1.15 ± 0.22 mm, respectively. The intracanal temperature reached 32.9 ± 0.8 and 33.2 ± 1.0°C after 3 min of application of irrigation solutions set at 22 or 45°C, respectively. The tested instruments exhibited diverse changes in their shapes and lengths at varying temperatures. Despite the temperature of the irrigation solution, the intracanal temperature consistently remained lower than the intracanal temperature once equilibrium was reached. This highlights the importance of considering the temperature of irrigation solution during invitro testing of endodontic instruments.

A data integration framework of additive manufacturing based on FAIR principles

Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration.Graphical abstract

Facial Feature-Based Attention Tracking System for Enhanced Online Learning Engagement

Recognizing and enhancing student engagement is crucial for improving learning outcomes, particularly in the context of online classes where monitoring can be challenging. Traditional methods of attendance tracking, such as calling out names, are impractical and susceptible to manipulation in the virtual environment. Students might appear 'online' without actively participating, and the absence of video feeds makes it difficult for teachers to verify attendance and attention. In order to realize a highly efficient and robust attendance management and engagement level prediction system for online learning, In the proposed System, the learner’s face is monitored by a video camera while attending a video lecture. Facial features were analyzed to predict reaction time (RT) to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. Then apply a machine learning method, light Gradient Boosting Machine (LightGBM), to estimate RTs from facial features extracted as action units (AUs) corresponding to facial muscle movements by an open-source software (OpenFace). This project is to develops a user-friendly system integrated with private online learning and attendance recording system for teachers that can automatically record students ‘engagement state and attendance then generate attendance reports for online classrooms. It encompasses a novel design using the AI based FFCNN (Face Fiducial Convolution Neural Network) model to capture face biometric randomly from students’ video stream and record their attendance automatically. This integrated solution not only streamlines attendance management but also provides valuable insights into students' engagement levels through facial feature analysis.

Place and Independence Are Formed by Moving Furniture

In contemporary architectural design, a noticeable trend emerges in the meticulous planning of common areas, with an emphasis on fostering the free movements of users within these areas. It is important to consider the architectural elements that influence user behavior in these environments. Previous studies have delved into the relationship between architecture and user activity, often through questionnaires and activity surveys conducted in arbitrary spaces. However, such data are discontinuous and only capture a portion of the space. Therefore, in this study, continuous and extensive recordings were performed in an arbitrary space using a video camera to capture the entirety of the area. Consequently, we were able to comprehend the relationship between architecture and people’s activities and observe the formation of user autonomy through their decisions to rearrange furniture. Furthermore, an “Open Pose” system was used to detect people and generate a user stay distribution chart. This was achieved by converting it into a plan view using perspective projection transformation, thereby obtaining objective data automatically. Consequently, we verified the feasibility of conducting visually comprehensible processing using tools like “Open Pose”, which was used in one segment of the experiment.

Effect of transportation distance and lairage time on selected behaviors and carcass parameters in zebu cattle-a study using the animal focal sampling method.

The objective of this study was to evaluate the effect of the transport distance and lairage time on behavioral indicators of zebu beef cattle in the resting pen of a slaughterhouse using the focal animal technique. Eight lots of male zebu cattle, Nelore, aged approximately 4 years old, weighing on average 500 ± 28.5 kg-1, from different municipalities, transported by land, were evaluated. The lots were grouped into two categories: short distance (> 500 km) and long distance (> 500 km). Five focal animals per evaluated lot were used in each cycle of behavior observation, randomly chosen, and visually identified. The animal focal sampling method was used, with instantaneous recording, every 5 min per animal/h. A video camera was used and placed at a fixed point in the corral. After slaughtering, bruises and carcass pH were evaluated. The respiratory rate (RR) was measured in two moments, 30 min after unloading (RR_I) and 1 h before slaughtering (RR_F); behavioral data were divided into four lairage periods (1st, 2nd, 3rd, and 4th) equally and evaluated as repeated measures over time. The lairage time lowered the behaviors of headbutting (NCHBs), pushing (PsH), mounting (MT), and chasing away (CA), (p = 0.0001), and these behaviors were more evident in animals transported for short distances in the initial resting times, decreasing as they remained in the resting pen. There was effect of distance on the initial and final pH of the carcass, being greater in the short distance (p = 0.047). There was no effect of distance or lairage time on initial or final RR (p > 0.05). In addition, animals transported over long distances showed less carcass injuries, which were observed during post mortem inspection. It is concluded that animals transported over short distances present a greater number of agonistic behaviors, such as NCHBs, MT, PsH, and CA, predisposing to higher rates of bruising during lairage and more accentuated changes in the final pH of the meat.

Automatic Miscalibration Detection and Correction of LiDAR and Camera Using Motion Cues

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift. First, a monitoring algorithm that can continuously detect the miscalibration in each frame is designed, leveraging the rotational motion each individual sensor observes. Then, as sensor drift occurs, the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion, which is further utilized to align the drifted LiDAR scan with the camera image. Finally, the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift, then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.

Use of Artificial Intelligence to Monitor the Reliability of Removable Load-Handling Devices

Introduction. The malfunction of removable load-handling devices (RLHD) poses significant production risks. That is why research in this field is relevant. The problem has often become a topic of scientific investigation. The authors propose using artificial intelligence more extensively to monitor the state of RLHD. This paper presents a study on how to improve the machine vision model to better identify the absence of locks on RLHD hooks. A probable occurrence of such an issue in production is noted. A storage and monitoring system for RLHD condition is proposed. The aim of this study is to demonstrate the potential for further training of neural networks to significantly enhance the efficiency of RLHD monitoring, ensuring their safe use.Materials and Methods. The work is based on the results of a survey conducted at the LLC “KZ Rostselmash” plant from 2022 to 2023, involving 144 RLHD. Mathematical statistics methods were used to process the data. A neural network model previously trained using the YOLO computer vision algorithm was studied. It was retrained taking into account the norms of the rejection of RLHD, specified in federal rules and standards. Images of RLHD with defects and missing parts were collected from these sources and used to create a training database. The database was expanded by augmentation. The Roboflow platform was used for work.Results. The array of images used for further training of the neural network was divided into three samples: training (88%), validation (8%) and test (4%). These samples were used to train and validate its results. The training was completed after 260 epochs, with a steady increase in accuracy. The neural network model of computer vision obtained in this way automatically detected a common defect in the RLHD hook — the absence of a lock. Its performance was assessed using three indicators: average accuracy (94%), prediction accuracy (88.8%) and response (89.2%). The neural network could receive images from a video camera in real-time and recognize hook defects. During the RLHD inspection at the Rostselmash plant, a grab for lifting engines was found to have all three hooks defective — without locks. To avoid such situations, at the end of work, it was recommended to place the RLHD on a special stand equipped with a microcontroller device that could monitor for a number of potential issues using radio frequency identification.Discussion and Conclusions. The main goal of this proposed solution is to detect and address signs of non-compliance with the established standards. This task can be implemented in facilities that use lifting equipment. In this case, the timely noticed RLHD defects will allow preventing production incidents. As a result, material damage can be reduced and injury statistics improved.

Camera-Based Crime Behavior Detection and Classification

Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural networks to identify the crimes happening in the real world. Through our approach, it will be easier for police agencies to discover and assess criminal activity more quickly using our method, which will reduce the burden on their staff. In this paper, we aim to provide anomaly detection using surveillance videos as input specifically for the crimes of arson, burglary, stealing, and vandalism. It will provide an efficient and adaptable crime-detection system if integrated across the smart city infrastructure. In our project, we trained multiple accurate deep learning models for object detection and crime classification for arson, burglary and vandalism. For arson, the videos were trained using YOLOv5. Similarly for burglary and vandalism, we trained using YOLOv7 and YOLOv6, respectively. When the models were compared, YOLOv7 performed better with the highest mAP of 87. In this, we could not compare the model’s performance based on crime type because all the datasets for each crime type varied. So, for arson YOLOv5 performed well with 80% mAP and for vandalism, YOLOv6 performed well with 86% mAP. This paper designed an automatic identification of crime types based on camera or surveillance video in the absence of a monitoring person, and alerts registered users about crimes such as arson, burglary, and vandalism through an SMS service. To detect the object of the crime in the video, we trained five different machine learning models: Improved YOLOv5 for arson, Faster RCNN and YOLOv7 for burglary, and SSD MobileNet and YOLOv6 for vandalism. Other than improved models, we innovated by building ensemble models of all three crime types. The main aim of the project is to provide security to the society without human involvement and make affordable surveillance cameras to detect and classify crimes. In addition, we implemented the Web system design using the built package in Python, which is Gradio. This helps the registered user of the Twilio communication tool to receive alert messages when any suspicious activity happens around their communities.

Analysis of the Polarization Distribution and Spin Angular Momentum of the Interference Field Obtained by Co-Planar Beams with Linear and Circular Polarization

Interference of two and four light beams with linear or circular polarization is studied analytically and numerically based on the Richards–Wolf formalism. We consider such characteristics of the interference fields as the distribution of intensity, polarization, and spin angular momentum density. The generation of light fields with 1D and 2D periodic structure of both intensity and polarization is demonstrated. We can control the periodic structure both by changing the polarization state of the interfering beams and by changing the numerical aperture of focusing. We consider examples with a basic configuration, as well as those with a certain symmetry in the polarization state of the interfering beams. In some cases, increasing the numerical aperture of the focusing system significantly affects the generated distributions of both intensity and polarization. Experimental results, obtained using a polarization video camera, are in good agreement with the simulation results. The considered light fields can be used in laser processing of thin films of photosensitive (as well as polarization-sensitive) materials in order to create arrays of various ordered nano- and microstructures.

Video saliency prediction for First-Person View UAV videos: Dataset and benchmark

Visual saliency prediction plays a crucial role in Unmanned Aerial Vehicle (UAV) video analysis tasks. In this paper, an eye-tracking dataset of the immersive viewing of videos captured from a First-Person View (FPV) of UAVs is developed, which consists of 200 video clips captured by DJI FPV drones, with a resolution of 4K QHD. The videos cover six different genres and fourteen unique scenes. To study human visual attention in watching FPV videos, fixation points are recorded using an eye tracker integrated into a VR headset. Based on the dataset, a simple yet effective FPV UAV video Saliency prediction model (FUAVSal) is proposed as a baseline, considering spatial–temporal feature, camera motion information and FPV prior. To establish benchmarks for saliency prediction in immersive FPV UAV video viewing, sixteen computational models are evaluated on this dataset. Detailed quantitative and qualitative comparisons are provided. The developed dataset and benchmarks aim to facilitate research on visual saliency prediction for First-Person View UAV videos.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

Bundle Adjustment of Aerial Linear Pushbroom Hyperspectral Images with Sub-Pixel Accuracy

AbstractLinear pushbroom (LP) cameras are often used in airborne hyperspectral imaging (HSI). Orthoimages are generated for HSI analyses but these require accurate camera orientations from bundle adjustment. However, the limited image overlap leads to an over-parameterization in the bundle adjustment with six degrees of freedom per image exposure, i.e., LP image line. Feature-based matching based on salient key points in small image neighborhoods cannot be readily applied to LP image lines, since each LP image line is only a single pixel wide in one of the image dimensions. A naive mosaicing of consecutive LP image lines leads to unacceptably large errors owing to the relative camera motion within these mosaics, even for a stabilized camera. Thus, a new method that allows the use of established methods for feature-based matching from aerial LP image lines is presented, and observations are retrieved and used in the bundle adjustment. The examination of the spatial misregistration between spectral bands in HSI cameras, i.e., the chromatic aberration, is also examined. The method assumes a stabilized camera system with a processed global navigation satellite system aided inertial navigation system solution. The bundle adjustment is done by estimating trajectory corrections in time intervals and retrieving discrete trajectory estimates from cubic spline interpolation. An experiment was conducted to demonstrate the method. The chromatic aberration is shown to be of sub-pixel level in the LP HSI camera, and the resulting planimetric accuracy (normalized median absolute deviation) from the bundle adjustment is $$\sim 1/4$$ ∼ 1 / 4 of the ground sampling distance in each of the north and east components. The accurate estimates from the bundle adjustment are shown to be suitable for high-quality orthoimage generation.

Optimizing Camera Motion with MCTS and Target Motion Modeling in Multi-Target Active Object Tracking

In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is to achieve continuous tracking of targets through real-time control of camera. This form of active camera control can be applied to unmanned aerial vehicles (UAV), intelligent robots, and sports events. Our work is conducted in an environment featuring multiple cameras and targets, where our goal is to maximize target coverage. Contrasting with previous research, our work introduces additional degrees of freedom for the cameras, allowing them not only to rotate but also to move along boundary lines. In addition, we model the motion of target to predict the future position of the target in environment. With target’s future position, we use Monte Carlo Tree Search (MCTS) method to find the optimal action of camera. Since the action space is large, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search method, so as to find the optimal action more efficiently. We establish a multi-target 2D environment to simulate several sports games, and experimental results demonstrate that our method can effectively improve the target coverage. The code is available at: http://github.com/HopeChanger/ActiveObjectTracking .

Production of sounds by squirrelfish during symbiotic relationships with cleaner wrasses

Examples of symbiotic relationships often include cleaning mutualisms, typically involving interactions between cleaner fish and other fish, called the clients. While these cleaners can cooperate by removing ectoparasites from their clients, they can also deceive by feeding on client mucus, a behavior usually referred to as “cheating behavior” that often leads to a discernible jolt from the client fish. Despite extensive studies of these interactions, most research has focused on the visual aspects of the communication. In this study, we aimed to explore the role of acoustic communication in the mutualistic relationship between cleaner fishes and nine holocentrid client species across four regions of the Indo-Pacific Ocean: French Polynesia, Guam, Seychelles, and the Philippines. Video cameras coupled with hydrophones were positioned at various locations on reefs housing Holocentridae fish to observe their acoustic behaviors during interactions. Our results indicate that all nine species of holocentrids can use acoustic signals to communicate to cleaner fish their refusal of the symbiotic interaction or their desire to terminate the cooperation. These sounds were predominantly observed during agonistic behavior and seem to support visual cues from the client. This study provides a novel example of acoustic communication during a symbiotic relationship in teleosts. Interestingly, these vocalizations often lacked a distinct pattern or structure. This contrasts with numerous other interspecific communication systems where clear and distinguishable signals are essential. This absence of a clear acoustic pattern may be because they are used in interspecific interactions to support visual behavior with no selective pressure for developing specific calls required in conspecific recognition. The different sound types produced could also be correlated with the severity of the client response. There is a need for further research into the effects of acoustic behaviors on the quality and dynamics of these mutualistic interactions.