Real-time tracking system using precise space and time information of the hit

Achieving the detection and tracking of moving targets has been widely applied in all fields of today's society. Because of the shortcomings of traditional video tracking system, this paper proposes a novel method for designing video processing system based on hardware design of FPGA and DSP, and moving target in video can be detected and tracked by this system. In this system, DSP as the core of the system, it mainly completes the processing algorithms of video and image data, FPGA as a coprocessor, responsible for the completion of the processing of external data and logic. The hardware structure, link configuration, program code and other aspects of system are optimized. Finally, through the experiment, the input frame rate of video is 40frames/s, and the image resolution is 512pixels × 512pixels, median 16bites quantitative image sequence, the system can complete the relevant real-time detection and tracking algorithm and extract targets position of image sequences correctly. The results show that the advantage is that this system has powerful operation speed, real time, high accuracy and stability.

Due to the rapid growth of computer science, image processing technology has recently been widely applied to various fields such as videoconferencing, computer vision and face tracking. However, the development of tracking systems is at a standstill because most studies have used the still camera and pure background. In an effort for advancement, this study proposes a wireless real-time tracking system based on background subtraction, the Gauss motion model, and the Otsu algorithm, to identify a moving object and then pursue it. This system consists of (a) a tracking platform with ARM and robot and (b) an image recognition system that regulates the location of the robot to trace the object and identifies the location of the object from the real-time image, respectively. In practice, the results of this study show that the proposed system can effectively trace the specific moving object and mark the object in the center of the view.

With the advancement of telematics and intelligent transportation systems, the need for real-time monitoring of vehicle performance and location has become increasingly critical, particularly for large fleet operations. This paper presents the design and implementation of a real-time fleet tracking and diagnostic system that integrates Controller Area Network (CAN) bus communication with Global Positioning System (GPS) technology. The proposed system acquires essential vehicular parameters—including engine speed (RPM), coolant temperature, vehicle speed, fuel level, and diagnostic trouble codes—directly from the vehicle’s electronic control units via the CAN protocol. Concurrently, GPS data provides continuous location updates. All information is transmitted to a centralized server using wireless communication (e.g., GSM/4G) for real-time visualization and analysis via a web-based dashboard.

The increase in popularity of fitness and at-home fitness has illuminated a major deficiency with traditional fitness tracking; the absence of immediate motion analysis and corrective feedback. Many conventional methods of instruction, as well as wearables, lack the system feedback of prompting users for proper movements, which can lead to many real workout injuries. The proposed system is the "Real-Time Exercise Tracking System". The proposed project is an intelligent method of intervention that uses computer vision and deep learning to provide a solution to the problem of lacking immediate feedback. The objective of the system is to accurately assess the form a user is exercising in, and properly provide the user with effective feedback on their form in real-time. The system utilizes a combination of MediaPipe for estimating accurate user pose, and a machine learning model to assesses the required movements of the user. The application provides the user with real-time voice and visual feedback on motion awareness. The application will assess the user's exercise awareness in terms of AI-based form correction, real-time performance analytics, propose new workouts based on previous fitness activities, and mitigate risk of injuries with alerts. The project will contribute to filling the gap between unmonitored home activity and professional fitness education, thus creating a safer, more effective, and happy fitness experience for users. Keywords – Real-Time Tracking, Pose Estimation, Computer Vision, MediaPipe, Fitness Technology, Deep Learning, Form Correction

Designing a real-time visual tracking system to catch goldfishes is a very complex task because a fish’s precise relative 3D positions inside a water tank are difficult to determine. In this paper, we present a new visual servo control system using stereo vision to acquire the 3D positions of goldfishes. The continuously adaptive mean shift (CAMSHIFT) algorithm and optical flow vision tracking methods are applied to provide feedback of a tracked fish’s real-time position and motion trajectory in a water tank at a high frame-rate. Two low-cost, well calibrated cameras are used to track the goldfishes and a 5-axis robot manipulator is used to catch a specified goldfish. The designed visual tracking and servo system is less sensitive to lighting influences and thus performs more efficiently. Experiments of the proposed method yielded very good results as the real-time 3D vision successfully tracked two fishes and guided the manipulator to catching one of them in a net attached to its end-effector.

Software-based eye tracking systems, often leveraging libraries like OpenCV, can encounter significant latency and through- put bottlenecks in demanding real-time scenarios, especially when processing high-resolution video streams. This paper presents an innovative FPGA-based solution to overcome these limitations, detailing the design and implementation of a real-time eye tracking system entirely in Verilog HDL for optimized performance and resource utilization. The primary objective is robust pupil tracking at 720P resolution and 60 frames per second (fps). The system utilizes an infrared camera for video acquisition, processes data on an VF-Ti60F100 development board, and displays the tracked pupil position on an HDMI monitor. Core processing stages include image preprocessing, gradient-based glint detection, and pupil localization within an extracted eye sub-image. Innovations embedded in the hardware design feature: a novel combined vertical pro- jection and pipelined centroid algorithm for efficient glint localization using only vertical image data and on-chip RAM; direct processing of compact eye sub-images centered on the detected glint; and a robust hardware-based blink detection mechanism analyzing glint stability. Experimental results demonstrate that the system successfully achieves real-time pupil tracking at 720p@60fps with high accuracy and extremely low latency, effectively processing input resolutions up to 1280x1024. This underscores the potential of FPGAs for developing high-performance, low-latency HCI applications.

This paper presents an investigation of the development of a real-time object tracking system that utilizes computer vision techniques and incorporates pan-tilt control mechanisms. The system architecture includes hardware components such as a camera and a pan-tilt mechanism, and software components such as object detection and tracking algorithms. To achieve efficient and accurate tracking, the Kalman filter, which uses a state-space model, was included in the object tracking process. The proposed system is implemented and evaluated through experiments to demonstrate its feasibility and effectiveness in real-world scenarios. The results indicate that the proposed system is capable of achieving real-time and accurate object tracking, demonstrating its potential for practical applications in various fields.

In this paper, we propose a real-time lane detection and tracking algorithm using a simple filter and Kalman filter to develop a Lane Departure Warning System(LDWS) that can be implemented in an embedded system. Our LDWS was realized on the I.MX6Q board, mounted on a test vehicle, and traveled about 1,000km to derive the experimental results. Experimental results show that our LDWS operates at 97% detection rate in the day time and 95% in the night time. The average processing time of the our LDWS is about 15frame per seconds.

A low-cost real-time two-dimensional PC-based ball tracking and control laboratory system is developed to teach real-time control system development with a real physical system instead of computer simulation. The system can be used for a single-student or a two-student competition for the best control algorithm. Training can be given on pattern recognition, modelling, stepper motor control, control strategies and other aspects.

A combined system comprising the LINIAC TrueBeam (Varian Medical Systems, Palo Alt, CA) and a new real-time tumor-tracking radiotherapy system, SyncTraX FX4® (Shimadzu Co., Kyoto, Japan), was installed in our institution. It consists of four pairs of an X-ray tube and a flat panel detector. The system was assessed on beam-on time delay between TrueBeam and SyncTraX FX4 and fluoroscopic dose during a real-time tracking. Delay time was measured by using a tumor-tracking radiotherapy phantom (CALIB PHANTOM ASSY, Shimadzu Co., Kyoto, Japan), in the cases of flattening filtered (FF) 6 MV photon beam, flattening filter-free (FFF) 6 MV photon beam, FF-10 MV photon beam, and FFF-10 MV photon beam with the LINAC TrueBeam. Half-value layer (HVL) in mm Al, effective kVp, and air-kerma rate during fluoroscopy were measured using a solid-state detector for the tube voltage (70–110 kV) and the current (50–100 mA). The LINAC delayed from the real-time tumor tracking system on beam-on by 140.9 ± 8.5, 119.8 ± 3.8, 126.1 ± 3.2 and 116.8 ± 9.7 ms for FF-6 MV, FFF-6 MV, FF-10 MV and FFF-10 MV, respectively. The HVL, effective kVp and air-kerma rates from X-ray tube #1 (X-ray tube #1 and #2 were embedded in the patient’s head side floor) were 4.98 ± 0.00 mm, 111.2 ± 0.1 kV and 9.14 ± 0.04 mGy/min for 110 kV X-ray at 100 mA. The HVL, effective kVp and air-kerma rates from X-ray tube #3 (X-ray tube #3 and #4 were embedded in the patient’s feet side floor), were 5.20 ± 0.00 mm, 110.0 ± 0.1 kV and 11.87 ± 0.06 mGy/min for 110 kV X-ray at 100 mA. These tube voltage and current are the maximum conditions of this study. The time delay of the real-time system is longer than the old system that used image intensifiers. The air-kerma rate from X-ray tube #3 was higher than that from X-ray tube #1.

Aiming at vehicle detection and tracking problems in video monitoring and controlling system, this paper mainly studies vehicle detection and tracking problems in conditions of high traffic density in daytime. This paper is distinguished by two key contributions. First, we develop an improvement — SEAP(Simple but Efficient After Process) which checks the detection results in an accurate way and is an after process of Adaboost [1] detector which used to detect car in every frame. Second, we propose a tracking algorithm named 4-states tracking algorithm based on Kalman[5] linear filter. Tracking results turn unsteady as traffic density grows higher because of much more false positives and false negatives appear. However, 4-states tracking algorithm can solve this problem in an easy way by introducing FSM (Finite State Machine) into tracking algorithm. Finally, we implement a real-time vehicle detection and tracking system with the upper methods. Experiments give good results in relative crowded Conditions.

Oliva-Lozano, JM, Martín-Fuentes, I, Granero-Gil, P, and Muyor, JM. Monitoring elite soccer players physical performance using real-time data generated by electronic performance and tracking systems. J Strength Cond Res 36(11): 3224-3228, 2022-The aims of this technical report were to analyze the validity of real-time data collected by electronic performance and tracking systems (EPTS) and investigate the effect of varying real-time receiver's position on the real-time data collected. Physical performance data were collected from professional soccer players using EPTS. In addition, 3 real-time receivers, which were placed in different positions (i.e., central area of the stadium stands and right and left technical areas), were used to collect real-time data. The real-time data collected by each receiver were visualized on SVivo and compared with the data downloaded directly from the device on SPro. The results showed no statistically significant differences between the data collected by the real-time receivers compared with postsession data in any variable ( p > 0.05), except for total distance and high-speed running distance covered, which showed significant differences but trivial effect size ( p < 0.05; d = 0.01). The coefficient of determination ( R2 ) and intraclass correlation coefficient were greater than 0.97 and 0.99, respectively. Regarding the analysis of varying the receiver's position on the real-time data collected, the results showed that there was no significant effect of the receiver's position on any variable ( p > 0.05). Therefore, valid physical performance data may be obtained by real-time tracking systems such as SVivo, regardless of the position of the real-time receivers and distance to the players. Specifically, high-intensity running actions, distances covered at low and high speed, and accelerometer-derived variables such as player load may be accurately tracked by this real-time tracking software.

This paper presents the design, development, and performance analysis of a real-time solar tracking system integrated with a Data Acquisition System (DAS), Maximum Power Point Tracking (MPPT) algorithm, and a Lithium Iron Phosphate (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{LiFePO}_{4}$</tex>) battery storage supervised by a Battery Management System (BMS). The proposed system employs a microcontroller-based DAS unit to maximize solar energy capture through dynamic panel orientation and to monitor environmental parameters in real time. A custom-designed MPPT charge controller ensures optimal power conversion, while the BMS-controlled <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{LiFePO}_{4}$</tex> battery pack (IFR32700N60, 6000 mAh, 3.2 V) manages charging, discharging, and cell protection for enhanced safety and lifespan. The hardware integrates a bipolar stepper motor driver for panel tilt adjustment, operated via a PIC-based embedded controller and an RTC for daylight tracking. Load dynamics are measured using a potential-divider-based current sensor, while a temperature-stable operational amplifier filter improves signal accuracy. Experimental validation using MATLAB and Excel-based post-processing demonstrates high efficiency, modularity, and adaptability for smart-grid and off-grid solar applications.

Real-time human eye recognition and tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This paper presents an analysis of pupil response to video structure and content. A consumer device that can assess user response to a video can provide better experiences with approaches such as real-time content adaptation. The first set of experiments involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show that pupil constrictions and magnitude of constrictions vary with content. Significant changes in video and scene cuts led to sharp constrictions. User response to videos can provide insights that can improve subjective quality assessment metrics. This paper also presents an analysis of the pupillary response to quality changes in videos with the second set of experiments. The results of the tests show pupil constrictions for noticeable changes in perceived quality. Using real-time eye tracking systems for video analysis and quality evaluation can open a new class of applications for consumer electronic systems.

The motorized antenna mechanism is the central part of healthy satellite communication using a real-time motion tracking system. Typically, parallel manipulators are employed in the space industry for tracking the satellites with the antenna mounted on the end-effector. However, the workspace limitations of the parallel manipulators are highly restricted in terms of the end-effector's linear and angular motions compared with the serial manipulators. In order to take the privilege of a serial manipulator advantages, the antenna is mounted on the ABB irb6600 manipulator's end-effector while only generating 2-degree-of-freedom (2-DoF) motions for simplification. Unfortunately, the current system cannot track the satellite motion accurately as it cannot generate the smooth motion while following a path, especially when the end-effector is in the vertical position. In this study, a real-time motion control tracking system using the 6-DoF motion of the ABB irb6600 robot is designed and developed using TCP/IP communication technique between MATLAB and IRC5 controller aiming to accurately track the satellite path with the ability to decrease the jerkiness of the motion. The proposed method has been tested in simulation environment using the small prototype of the ABB robot (irb120) while Simulink Desktop Real-Time and RoboStudio are used. The inertial measurement unit (IMU) sensor is used to prove the tracking accuracy of the proposed method and elimination of the jerky motions using the proposed algorithm.