A real-time color feature tracking system using color histograms

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

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.

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.

Real-time target tracking is a usual task for humans despite the neural delays during the nervous system’s axonal transfer and neural processing. A plausible explanation is that the human brain employs predictive mechanisms to compensate for the delay. Inspired by the brain, this paper adopts a prediction network based on spiking neural networks (SNNs) to implement a real-time tracking task on a neuromorphic chip with low power consumption. The SNN-based prediction network outperforms the long short-term memory (LSTM) network on a small dataset and reduces 90% to 98% computations compared with LSTM. The quantized SNN-based network is deployed on a neuromorphic chip, and it takes 25ms and only 442 626nJ for a single prediction. The tracking performance of the system is also verified in real-life scenarios. Furthermore, the proposed real-time target tracking system can be easily ported to other neuromorphic platforms.

This paper proposes a real-time location tracking system using a GPS module for different mobile devices and multiple users. It focuses on the management and observation of a majority of people can be foretold. Traditional RF, wireless ATM networks, or wireless cellular radio networks-based location tracking services encounter challenges. These include poor radio link quality and resource consumption (i.e., radio bandwidth) due to the users' mobility, thus necessitating managing their position. Our proposal can service a real-time location tracking service for multiple parties using TCP/IP wireless Internet protocol and GPS application in a mobile environment. The user may acquire and manage location information of specified subjects, who require individual care in real-time. Such users include those requiting specific protective measures, children, and the elderly. The proposed GPS and wireless network-based real-time location tracking system may be extended to various application domains that detect user location information and utilize this anytime, anywhere.

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.

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.

We report on the design of a real-time tracking system using precise space and time information of the hit. Tracking detectors with about 150 ps time resolution are already available and intense R&D is in progress to improve the performance. We describe the implementation of a real-time tracking algorithm in commercial FPGAs for a prototype tracking system based on 8 planes of silicon sensors used as a case study. We demonstrate that the use of the additional time information in the tracking algorithm allows to suppress noise hits while reducing fake track combinations and improving real-time track reconstruction. The system provides offline-like quality tracks with sub-microsecond latency and it is capable to determine the time of the track with picosecond resolution assuming 10 ps resolution for the hits.

Laser spot detection and tracking play a critical role in laser techniques. However, traditional detection and tracking systems tend to be bulky and lack portability. Therefore, there is a growing emphasis on developing high-performance and miniaturized systems based on the field programmable gate array (FPGA). In this paper, a novel parallel multi-target detection and determination algorithm is proposed to address the issue of current FPGA-based systems' ineffective detection of laser spots in complex environments. Our simulation results demonstrate that the algorithm can effectively detect laser spots in complex environments. It can process a frame with an 800 × 480 resolution in only 7.88ms at a 50MHz image processing frequency, which means it can process more than 100 f/s and meet the real-time detection requirements. Such excellent detection performance is challenging to achieve with central processing units and advanced RISC machine microprocessors. Then, the algorithm is further deployed on an FPGA to build a prototype laser spot detection and tracking system. Practical tests show that the system can achieve a spot detection accuracy of around 90% under different luminous intensities, indicating excellent robustness of the designed algorithm. Besides, with the use of a piezoelectric actuator, speedy and precise tracking of the laser spot is implemented. The characteristics of speedy response, self-latching in power off, and no electromagnetic interference of the piezoelectric actuator give the system tremendous advantages in developing high-precision wireless communication control technology, which further broadens the application of the proposed system.

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.

To measure respiratory motion of the thoracic wall region in dogs using a real-time motion tracking system and compare the amount of respiratory motion between dogs positioned with and without a vacuum-formable cushion. 8 healthy adult mixed-breed dogs (median weight, 23 kg). Dogs were anesthetized and positioned in sternal and dorsal recumbency with and without a vacuum-formable cushion. Three-dimensional movement of anatomic landmarks was measured with a real-time motion capture system that tracked the locations of infrared light-emitting diodes attached externally to the dorsal or ventral and lateral aspects of the thoracic wall. Dogs positioned in sternal recumbency had significantly less cranial-to-caudal and left-to-right respiratory motion at the lateral aspect of the thoracic wall, compared with dogs positioned in dorsal recumbency, whether or not a cushion was used. For dogs treated in sternal recumbency, use of a cushion significantly increased the peak displacement vector (overall movement in 3-D space) for 3 of 4 marker locations on the dorsal thoracic wall. As respiratory rate increased, respiratory motion at the lateral and ventral aspects of the thoracic wall decreased when data for all dogs in dorsal recumbency were evaluated together. Associations between respiratory rate and respiratory motion suggested that the use of rapid, shallow ventilation may be beneficial for dogs undergoing highly conformal radiation treatment. These results provide a basis for further research on respiratory motion in anesthetized dogs.

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.

This study aims to evaluate the dosimetric accuracy of the Lung with Respiratory (LWR) technique in the Radixact Synchrony real-time tumor tracking system using data from actual patients undergoing lung SBRT. This retrospective study included 20 patients treated with the Synchrony LWR technique for primary lung cancer or lung metastases. Mid-ventilation breath-hold CT and 4DCT images were acquired for treatment planning and tumor motion evaluation. Gamma analysis was performed using the PTW 1500 detector. Three verification plans were generated for each patient: one for gamma analysis without motion (GAWoM), one for point dose measurement with motion (PDWM), and one for gamma analysis with motion (GAWM). Patient specific respiratory motion was simulated using a CIRS motion platform. The relationship between respiratory patterns, treatment plan parameters, and the dose delivery accuracy of the Synchrony system was evaluated using correlation analysis. The PDWM results demonstrated a strong agreement between the planned and measured doses, with a maximum difference of less than 2%. The gamma passing rates of 3D dose measurements for all patients' Synchrony plans were found to be within the clinically acceptable level. While GAWM results were slightly lower than GAWoM results, correlation analysis revealed a strong positive relationship between the two. A moderate negative correlation was observed between the modulation factor and GAWM, similar to GAWoM. Additionally, a strong negative correlation was observed between beam-on time and both GAWoM and GAWM. Furthermore, no significant correlation was found between GAWM and respiratory pattern-related variables, including superior-inferior and lateral motion amplitude or respiratory rate per minute. The results of the correlation analyses indicate that the dose delivery accuracy of the Synchrony LWR technique is comparable to that of static target irradiation, independent of the patient's respiratory pattern or the planning parameters used.

The importance of the real-time object recognition and tracking field has been growing steadily due to rapid advancement in the computer vision applications industry. As is well known, the mean-shift algorithm is widely used in robust real-time object tracking systems. Since the mentioned algorithm is easy to implement and efficient in object tracking computation, many say it is suitable to be applied to real-time object tracking systems. However, one of the major drawbacks of this algorithm is that it always converges to a local mode, failing to perform well in a cluttered environment. In this paper, an Optical Flow-based algorithm which fits for real-time recognition of multiple moving objects is proposed. Also in the tests, the newly proposed method contributed to raising the similarity of multiple moving objects, the similarity was as high as 0.96, up 13.4% over that of the mean-shift algorithm. Meanwhile, the level of pixel errors from using the new method keenly decreased by more than 50% over that from applying the mean-shift algorithm. If the data processing speed in the video surveillance systems can be reduced further, owing to improved algorithms for faster moving object recognition and tracking functions, we will be able to expect much more efficient intelligent systems in this industrial arena.

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.