Real-time Algorithm Research Articles

Heuristic-based image stitching algorithm with automation of parameters for smart solutions

The analysis of two-dimensional images enables the detection of structures, objects and their further classification. In the Internet of Things, images are data obtained from cameras or projections of signals. In this paper, we propose a hybridization of the classic RANSAC approach with a selected heuristic algorithm like Grey Wolf Optimizer. This solution allows for increasing the ability to adjust images by analyzing the detected key points of the image. Such hybridization is interesting in terms of the possibility of obtaining stitching accuracy. Moreover, we have introduced the automation of parameter selection to increase the accuracy and speed of the real-time algorithm. For this purpose, an analysis of the results of the algorithm for combining video frames into one large image allowed for a significant reduction in the amount of data processing time. The proposed algorithm was tested on various heuristic algorithms, where their differences and impact on the operation of the method were indicated and discussed. Moreover, the proposition was tested in practical application by automatically creating an image based on video frames recorded by the drone.

Two-stage robust scheduling and real-time load control of community microgrid with multiple uncertainties

With the increasing popularity of wind and solar generation, the stable and economic operation of the community microgrid system become more challenging. This paper is concerned with the robust scheduling and real-time control of demand response (DR) air conditioning (AC) loads for community microgrid. Firstly, a two-stage robust scheduling model coupled by the day-ahead dispatch and real-time dispatch is established by considering the uncertainties of wind/photovoltaic power generation and loads. Moreover, the incorporation of multiple interval uncertain sets is introduced to characterize the uncertainties related to wind power, photovoltaics, and load, thereby reducing the conservativeness of the model. Secondly, the real-time control of AC loads by adjusting the on/off actions and temperature setpoints with limited impact on user comfort so as to track the optimized electricity consumption curve. Finally, the simulation results show the effectiveness and robustness of the proposed optimization dispatch and real-time control algorithm.

An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings

Accurately identifying the flowers and fruits of strawberry seedlings in the greenhouse is the key to automated flower and fruit thinning, which can improve efficiency and reduce labour costs in the cultivation. To address the challenges resulting from the small size, similar colour, and overlapping occlusion of strawberry seedling flowers and fruits, this paper proposes a real-time recognition algorithm (Improved YOLO) for accurately identifying them. Firstly, a Swin Transformer prediction head on the high-resolution feature map of YOLO v7 was constructed to better utilise spatial location information to enhance the detection of small target flowers and fruits, and improve the model's spatial interaction and feature extraction ability in scenes with similar colours and overlapping occlusions. Secondly, the GS-ELAN Optimisation Module for neck of network by GSConv was constructed to suppress shallow noise interference from the high-resolution prediction head and mitigate the increase of parameters resulting from high-resolution prediction heads. The experimental results showed that the Precision(P), Recall(R), and mean Average Precision (mAP) of Improved YOLO are 92.6%, 89.6%, and 92.1%. In the meantime, the Improved YOLO algorithm achieves a frame rate of 45 f/s, satisfying the real-time detection requirements. It is 3.2%, 2.7%, and 4.6% higher than the precision, recall, and mAP of YOLOv7, respectively. The accuracy of this model for detecting flowers and fruits was 93.9% and 91.3%, the recall was 93% and 86.3%, and the average precision was 94.7% and 89.5%, respectively. The Improved YOLO algorithm has a high level of robustness and real-time detection performance, allowing it to quickly and accurately identify the flowers and fruits of strawberry seedlings and provides effective support for the automated management of flower and fruit thinning of strawberry seedlings in greenhouse environments.

Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots

Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network’s parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms reduction in processing time. Based on the typical home activities of older people and children, the dataset Grasp-17 was established for the training and testing of the proposed method. Using the TensorRT neural network inference engine of the developed service robot prototype, the proposed dynamic and real-time object detection algorithm required 29 ms, which meets the real-time requirement of smooth vision.

Algorithm for Recognition of Small Air Targets by Trajectory Features in Passive Bistatic Radar

Introduction. In the past few years, the rapid development and widespread use of unmanned aerial vehicles (UAVs) for solving a variety of tasks has created new threats. The problem of ensuring the safety of protected objects, especially in the area of critically important objects or in places with difficult ornithological conditions (airports, wind power facilities), is of particular importance. In this regard, the issue of detecting small air targets and recognizing their type and degree of danger is acquiring greater importance. This paper presents an algorithm for recognizing air targets based onartificial intelligence technology. The results of a comparative analysis of decision-making methods for recognizing small UAVs and birds based on their trajectory features are presented. The results obtained can be used in the development of systems for recognizing classes of small airborne targets in existing and future radar stations. Aim. Development of an algorithm for recognizing small air targets by trajectory features based on machine learning. Implementation and evaluation of the quality of decision-making methods in a given recognition problem. Materials and methods. Experimental data on the trajectories of UAVs and birds obtained in a passive bistatic radar system are used. The trajectory parameters of the targets and their statistical characteristics are calculated; a set of features for recognition is formed. Using the MATLAB software package, a program for implementing the recognition algorithm and analyzing the quality of decision-making methods was developed. Results. An algorithm for recognizing air targets based on artificial intelligence technology is presented. A comparative analysis of the six most common recognition methods based on machine learning (Naïve Bayes, decision trees, k-nearest neighbors, neural network recognition algorithm, support vector machine, random forests) was carried out, which showed that, under the conditions of this problem, the most effective are k-nearest neighbor method and support vector machine. Conclusion. The presented methods can be used to directly determine the class of targets from a set of marks of their trajectories. Further research will be aimed at developing and implementing a real-time recognition algorithm.

An intelligent online fault diagnosis system for gas turbine sensors based on unsupervised learning method LOF and KELM

The performance of gas turbine inevitably grades slowly in service. In order to obtain high-precision state assessment, an intelligent online real-time sensor fault diagnosis algorithm was proposed in this paper. This method can automatically establish an accurate diagnosis model in a short time, realize the rapid diagnosis of sensor faults, and can effectively solve the problem of operation data imbalance. Firstly, wavelet analysis quickly converts the high-dimensional time-series sensor signal into a low-dimensional feature vector. Then, the unsupervised method LOF was used to screen the abnormal sensor signals. Finally, the KELM is used to achieve fast online identify the fault modes of the sensors. This fault diagnosis system achieves a faster diagnosis speed while ensuring the accuracy. The effectiveness of the method proposed in this paper was verified on the operational data of a gas turbine, the diagnosis time is about 0.233 s, which greatly increases the diagnosis speed compared with other methods, at the same time, the proposed method can guarantee more than 95 % of diagnosis accuracy.

Predicting Various Architectural Styles Using Computer Vision Methods

Computer Vision (CV), subfield of artificial intelligence (AI), enables computers to process visual data and recognize objects. CV is widely used in, automotive, food industry and diseases diagnosis. AI achieves this by algorithms. One of the important algorithms based on object detection is YOLO (You Only Look Once), provides more accurate results with high processing speed. The aim of this study is to perform an object detection-based CV project, to determine the structures in given video belong to one of the architectural styles: Gothic, Baroque, Palladian, or Art Nouveau. The study consists of data set creation, data labeling, model creation and model training. Roboflow was used as the data labeling platform and YOLOv8 was used for model building and training phases. At the end of the process, the fact that the model predicts architectural styles with high accuracy in a short time revealed that the model is a successful real-time object detection algorithm, and it was emphasized that CV can be used in the field of architecture and can contribute to other fields related to architecture.

Semantic segmentation-based framework for concrete pouring progress monitoring by using multiple surveillance cameras

Traditional construction progress monitoring methods face challenges in real-time monitoring of concrete pouring due to performance limitations and issues with registration and occlusion. This study proposes a framework based on multi-camera semantic fusion for monitoring the progress of concrete pouring. The construction site images are first segmented into semantic probabilities by a deep neural network. Dempster-Shafer evidence theory is then applied for semantic fusion, providing a comprehensive depiction of the progress. To reduce registration errors with the Building Information Model (BIM), a real-time perspective transformation algorithm is proposed to compensate for slight camera movements. Finally, a semantic inference method based on fully-connected Conditional Random Fields (CRFs) is employed to address occlusion by leveraging semantic context and the BIM floor plan. Comparative analysis confirmed these modules’ performance, with a remarkable reduction in relative error from 9.60% to 0.26%, enabling great potential for the continuous real-time monitoring of concrete pouring progress.

Real-time Monitoring System for Driver Phone Usage Based on Improved YOLOv5s

In response to the impact of driver's violations, such as using a mobile phone, on vehicle safety during the driving process, we propose an improved real-time monitoring algorithm based on YOLOv5s with lightweight optimization. Firstly, we replace the C3 module (CSP Bottleneck with 3 convolutions) in the backbone network of YOLOv5s with a lightweight Ghost Module to reduce the model's parameter count, enhance detection speed, and maintain inference accuracy unaffected, thus meeting the requirements of real-time monitoring. Secondly, we introduce the RepConv (Receptive Field Block) module into the Feature Extraction Network (PANet) structure to increase the neural network's receptive field for input images and further reduce the model's computational load. Experimental results show that the improved network achieves an mAP@0.5 of 95.7%, a detection speed of 140 FPS, and a model size reduction to 10.6MB, meeting the demand for real-time and reliable detection on embedded devices.

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

Real-Time Dynamic Gesture Recognition Algorithm Based on Adaptive Information Fusion and Multi-Scale Optimization Transformer

Gesture recognition is a popular technology in the field of computer vision and an important technical mean of achieving human-computer interaction. To address problems such as the limited long-range feature extraction capability of existing dynamic gesture recognition networks based on convolutional operators, we propose a dynamic gesture recognition algorithm based on spatial pyramid pooling Transformer and optical flow information fusion. We take advantage of Transformer’s large receptive field to reduce model computation while improving the model’s ability to extract features at different scales by embedding spatial pyramid pooling. We use the optical flow algorithm with the global motion aggregation module to obtain an optical flow map of hand motion, and to extract the key frames based on the similarity minimization principle. We also design an adaptive feature fusion method to fuse the spatial and temporal features of the dual channels. Finally, we demonstrate the effectiveness of model components on model recognition enhancement through ablation experiments. We conduct training and validation on the SCUT-DHGA dynamic gesture dataset and on a dataset we collected, and we perform real-time dynamic gesture recognition tests using the trained model. The results show that our algorithm achieves high accuracy even while keeping the parameters balanced. It also achieves fast and accurate recognition of dynamic gestures in real-time tests.

Exploring Data Acquisition and Real-Time Analysis Algorithms in Smart Manufacturing with a Focus on Automation and Inspection Technologies

The conducted research project is based on the process followed in the smart manufacturing method. It has been observed that there are different types of methods that have been used in the smart manufacturing process and among these methods most of the methods are directly associated with different modern technology and artificial intelligence as well. Along with the inclusion of automation systems in the smart manufacturing process, the incorporation of inspection technologies is also observed in the case of performing smart manufacturing systems. In association with these, the usage of an acquisition system and real-time analysis process is also observed as well. Independent steps are present inside the phases of production process of the smart manufacturing methods. The incorporation of automation processes significantly enhances the performance of individual phases within the smart manufacturing process, thereby raising the overall production standards. Automation minimizes the likelihood of human errors, data misplacement, and mishandling. Moreover, it proves highly advantageous in the data acquisition process, ensuring error-free data transfer through automated systems.

Development of a Real-Time Tractor Model for Gear Shift Performance Verification

Verification of the system is essential during the development of a tractor; however, there are cost and time limitations when verification is performed on an actual tractor. To solve this problem, we developed a tractor model for real-time simulation to verify the gear shift performance of the tractor and evaluate the control algorithm. This study examined and modeled a dual-clutch transmission (DCT)-type 105 kW class tractor. The proportional control valve, synchronizer, and clutch were modeled to accurately implement the shift behavior, and the developed individual model was verified based on actual individual product test data. The 45 s driving simulation was conducted to confirm whether real-time simulation of the entire developed tractor model was possible and whether it simulated the behavior of the target tractor well. The driving simulation results confirmed that the driving speed of the tractor model matched the engine speed, transmission gear ratio, and tractor specifications, and the gear shift performance of the tractor model according to the number of gears was confirmed. The simulated model thus satisfies the characteristics of the target tractor and can be used to verify the gear shift performance, indicating that the model can verify the performance of the control algorithm in real time.

A Deep Learning-Based Lightweight Model for the Detection of Marine Fishes

The species and population size of marine fish are important for maintaining the ecological environment and reflecting climate change. Traditional fish detection methods mainly rely on manual or traditional computer vision, which has disadvantages such as complex design, low detection accuracy, and poor generalization. The widespread use of ocean observation systems has accumulated a large number of images and videos, which makes the application of deep learning on marine fish detection possible. In this paper, a real-time high-precision fish detection algorithm based on YOLOv5s is constructed. Considering the enhancement of the channel representation and spatial interaction ability of the model, the attention mechanism and gated convolution are introduced, respectively, and GhostNet is introduced to lighten the model. Through a series of model comparisons, two improved models, S-Head-Ghost-Fish9 and S-SE-HorBlock-Head-Ghost-Fish9, are finally obtained. Compared with the original model, in terms of model size, the former reduces by 19% and the latter increases by 9.5%; in terms of computation, the former reduces by 15.7% and the latter reduces by 3.1%; in terms of detection speed, both take about 17 ms to detect a single image, and both can meet the real-time detection requirements; in terms of detection accuracy, the former improves by 3% and the latter by 3.6%. Compared with the latest detection algorithms of YOLOv6 and YOLOv8, the detection accuracy is slightly lower than 1%, but the model size and computation amount are only 1/3 to 1/2 of them. The improved models can help assess the population size and growth of the fish, which is of great significance in maintaining the stability of the fish population.

On-chip, non-invasive detection of all atrial arrhythmias for immediate and accurate characterization in adult rats

Abstract Background To monitor and record the occurrence and duration of cardiac arrhythmias in freely moving animals, ECG telemetry devices are often used. However, traditional telemetry devices require complex equipment unsuitable for on-site application in most animal housing facilities. Additionally, current atrial arrhythmias detection algorithms are mainly based on ECG R-R interval variability for atrial fibrillation (AF) detection. Such algorithms do not allow the detection of stable R-R interval atrial arrhythmias such as flutter. Thus there is currently a lack of non-invasive methods allowing automated detection of all atrial arrhythmias subtypes, thereby precluding in-depth research into general and subtype-specific mechanisms and treatment. Purpose Here we aim to create a non-invasive system allowing fast and accurate on-site detection and characterization of all atrial arrhythmias in rats. Methods Our atrial arrhythmia detector system was made of an on-skin ECG sensor, a low-power microprocessor unit, a large data storage unit and a battery. All components were assembled within a lightweight rat jacket. The filtered ECG signal was sent to the microprocessor allowing algorithm-based, real-time detection of atrial arrhythmias. The device allowed storage of ECG traces as well as arrhythmia characteristics (including type of arrhythmia and its duration). Atrial arrhythmias were induced by transesophageal atrial burst pacing in Wistar rats. These arrhythmia recordings were used to develop an AI detection method. Next, real-time detection algorithms based on R-R variability and AI were both integrated on-chip and compared using the same ECG data. Results ECG traces measured with our new system were similar to classical ECG measurements. Stored ECG traces provided the raw data for the development of an AI-based detection algorithm. The AI training was done using ECG data recorded from five different rats and reached 99.5% self-testing accuracy to detect all sub-types of atrial arrhythmias based on pre-categorized data. Using uncategorized data (n=25 from 5 rats) different from the training dataset, the AI algorithm could detect >80% of all types of atrial arrhythmias within 3s after initiation, while the rest could be detected within 10s. Though R-R interval based algorithm showed a high detection rate of 88% of AF within 10s, this method did not allow the detection of flutter or AF presenting regular R-R intervals within 1 minute after initiation. Conclusion Our atrial arrhythmia detection and analysis system provides a novel method for non-invasive investigation into atrial arrhythmias in rats and could easily be scaled for other species. It allows on-site fast and accurate detection and characterization of atrial arrhythmia subtypes without external equipment, thereby opening new possibilities for mechanistic studies and therapeutic testing, including closed-loop applications aiming for arrhythmia termination.

Abstract 16120: Efficacy of an Electronic Medical Record Alert System in Identifying Cardiac Implantable Electronic Device Infection

Introduction: Cardiac Implantable Electronic Device infection (CIED-I) is common, morbid, and frequently under recognized. Electronic Medical Record (EMR) based alerts have been proposed as a solution to improve detection of CIED-I. Hypothesis: Development of a real-time EMR algorithm to identify CIED-I can improve recognition of patients with CIED-I. Methods: An EMR (Epic, Verona, WI) alert system to identify inpatients with CIED-I was developed based on the presence of CIED and positive blood cultures. The presence of CIED was determined using patient problem list, structured data elements describing device implantation, and prior device interrogation. Alerts were generated each time a patient with CIED had a positive blood culture. We examined patients admitted between January 2020 - March 2023. Identified patients were adjudicated manually to confirm: (1) the presence of CIED and (2) true bacteremia versus contaminant based on patient and microbiological characteristics. Results: The algorithm generated 255 alerts, corresponding to 100 unique patients over 142 hospitalizations. Of these alerts, 213 appropriately identified 88 unique patients with positive blood cultures and CIEDs, resulting in an alert specificity of 84% and patient specificity of 88%. The remaining 42 (16.6%) alerts occurred in 10 patients who no longer had CIED due to prior CIED extraction (6 at the time of heart transplant and 4 with extraction without reimplantation) as well as 2 patients with erroneous blood culture identification (one positive syphilis antibody test and one lab documentation error). In patients with appropriate alerts, 27 patients had 4/4 positive blood culture bottles, 5 patients had 3/4, 10 patients had 2/4, 41 patients had 1/4, 2 patients had 2/2, and 1 patient had 1/2. The positive predictive value of the algorithm to identify true bacteremia was 0.65 (138/213). Conclusions: The algorithm has high specificity to identify patients with possible CIED infection. Improving the accuracy of device detection, by incorporating radiology imaging or history of extraction, could further improve specificity. Positive predictive value and alert fatigue could be improved by stratifying alerts by blood culture species and total number of positive blood cultures.

High-speed polarization tracking using thin film lithium niobate integrated dynamic polarization controller.

Dynamic polarization controllers (DPCs) are essential devices in various optical applications. We develop a thin film lithium niobate (TFLN) integrated DPC driven by the real-time implemented Jacobian control algorithm for fast polarization tracking. Experimental results demonstrate a high polarization tracking speed of 100 krad/s when targeting a specific linear state of polarization, with a low control loop delay of 420 ns, half-wave control voltages of 2.75 V, and a fast polarization restoring time of 1.6 us. Compared to previously reported integrated DPCs, the TFLN-based DPC achieves significantly higher tracking speed and lower loop delay. The results highlight the effectiveness of the Jacobian method and the outstanding performance of TFLN-based DPCs. The study opens up possibilities for further advancements in DPC solutions using TFLN technology.

Real Time Detection Algorithm for Escape Ladders based on YOLOv5s

In the detection of escape ladders in the context of smart construction sites, due to the relatively small target size of the escape ladder compared to the entire input image frame, significant environmental interference, and high missed detection and false detection rates, an improved YOLOv5s escape ladder real-time detection algorithm is proposed by combining the attention mechanism network. The model uses CSPLocknet53 as the backbone network for feature extraction, introduces the attention module CA, and integrates spatial and channel information, while increasing a small amount of computation, performance has been significantly improved. Optimize the network structure of YOLOv5s algorithm, strengthen shallow feature weights to enhance small target detection effectiveness, add attention mechanisms to increase the weight of small targets and their surrounding features, and use Mosaic methods for data augmentation to improve detection accuracy and recall. After multiple repeated experiments, these experimental results have proven that the optimized YOLOv5s algorithm for real-time detection of escape ladders has an average detection accuracy (accuracy, recall) of (81.8, 82.6). Compared with the traditional YOLOv5s algorithm, the accuracy and recall have been improved by 1.4% and 1.2%, respectively. The optimized YOLOv5s algorithm can effectively improve the detection accuracy of real-time detection of escape ladders, and improve the detection and resolution performance of small escape ladder targets.

Ensemble-Based Machine Learning Approach for Real-Time Person Counting in an Instant Attendance System

Real-time attendance systems have become indispensable in various domains, including educational institutions and workplaces, as they automate attendance tracking and improve efficiency. This paper introduces a robust real-time attendance system that combines OpenCV and the You Only Look Once (YOLO) model. By integrating computer vision and deep learning techniques, the system achieves accurate and rapid face detection and recognition. Our proposed system utilizes OpenCV, a powerful computer vision library, to capture video streams from cameras. The YOLO model, a cutting-edge real-time object detection algorithm, is employed to identify and localize faces within the video frames. Thanks to YOLO's efficiency, the system ensures real-time processing, enabling seamless attendance recording. To enhance accuracy, the system employs a two-step approach consisting of face detection and face recognition. During the face detection phase, the YOLO model detects bounding boxes around faces. Subsequently, the system matches these detected faces against a pre-existing database of enrolled individuals using face recognition techniques. To improve performance, transfer learning techniques are applied to fine-tune the YOLO model on a diverse dataset containingvarious face images. This adaptation process ensures high precision and recall rates, even in challenging conditions such as varying lighting and occlusion. Experimental results demonstrate the effectiveness of the proposed real-time attendance system, achieving a high accuracy rate suitable for practical applications. Its real-time performance allows for seamless integration into existing attendance management workflows, resulting in time savings and improved administrative processes. The core intention of this paperwork is to develop a GUI page using Python programming, which will display the number of students who are present and number of students who are absent along with the number of students when section details are entered manually. In addition to this, pictures of the students who are present are displayed on the framework. It is called the real-time face detection which is very beneficial for managingacademic activity.

Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5s (1.9-93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%-96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24h movie with an average F1 score of 0.91.