2000 fps real-time target tracking vision system based on color histogram

A high-speed vision system can be applied to color-histogram-based tracking at 2000 fps by hardware-implementing an improved CamShift algorithm. In the improved CamShift algorithm, the size, position, and orientation of a color-patterned object to be tracked in an image can be simultaneously extracted using only the hardware implementation of a color-histogram circuit module for calculating moment features of binary images quantized by 16 hue-based color bins. By hardware-implementing color-histogram circuit modules on a high-speed vision platform, IDP Express, the improved CamShift algorithm enables color-histogram-based tracking at 2000 fps for 512×511 pixel images in real-time. By installing our tracking system on a two-axis mechanical active vision system, we demonstrate the effectiveness of 2000 fps color-histogram-based tracking by performing several experiments of color-patterned objects, which are always tracked in the camera views even when they move rapidly under complicated backgrounds.

This study develops a highly reliable real-time visual tracking system capable of tracking a moving target in cluttered environment. The proposed system architecture can be divided into three main parts - the target detector, the target information extractor and the target tracker. The first part detects any moving object in the image, using the moving edges technique. Next, by integrating the information from the target detector and the attractable snake model, the second part automatically extracts the target's contour, illumination and color information to enable the target to be tracked. Finally, by adopting a new hybrid matching method, the third part enables the system to continuously track the target with great robustness and adaptability. Notably, this hybrid matching method incorporates the template matching method with a modified contour matching method. Furthermore, the visual tracking system developed in this study is established on a low-cost commercial camera platform, which can be controlled by a personal computer through an RS232 serial port. Finally, several experiments, including object tracking and human head tracking, are conducted to verify the robustness of the tracking 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.

First Demonstration of Combined kV/MV Image-Guided Real-Time Dynamic Multileaf-Collimator Target Tracking

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

As a key technology of detection and tracking system, moving target detection and tracking has attracted a great deal of attention. To achieve the goal of tracking target, conventional methods have to complete the detection of targets firstly, and then track the target. However, such kind of approaches require using the method of constant false-alarm rate to detect the whole frame image received at that time, which not only reduces the detection efficiency but also degrades the performance of target tracking. In order to implement real-time monitoring divers and other small moving targets under the conditions of strong underwater noise and clutter interference, an interactive algorithm for moving target detection and tracking is proposed based on acoustic image, which jointly analyzes the target detection system and the target tracking system. In the target tracking unit, the interactive algorithm takes advantage of data interconnection to remove the false targets produced by target detection; extrapolates the target's track, and predicts the location that the target may appear at the next moment by employing the Kalman filter. These messages will be used as priori information in the detection unit. In the detection unit, first of all, a gate is established whose center is the position that the target tracking unit predicted. For the image received at the next moment, detection in the predicted gate in implemented by using the method of constant false-alarm rate, followed by feeding back the detection results to the tracking unit. Compared with existed methods, the proposed algorithm can effectively improve the target detection efficiency and tracking performance, providing a better approach to monitor underwater small targets.

Purpose/Objective(s): Herein, we report the results of an IRB-approved phase II trial of Varian Trilogy/TrueBeam-based stereotactic ablative body radiotherapy (SABR) monotherapy for low-risk prostate cancer using the Calypso® System to provide real-time electromagnetic tracking of the prostate’s position during treatment delivery.Materials/Methods: A total of 102 low-risk patients completed protocol treatment between January 2007 and May 2009. A total dose of 40.0 Gy in 5 every-other-day fractions of 8.0 Gy was prescribed to the planning target volume. Target setup and tracking procedures were as follows: (1) the Calypso® System was used to achieve target setup prior to each fraction; (2) conebeam CT imaging was then used for correction of setup error and for assessment of target and organs-at-risk deformations; (3) after treatment delivery was initiated, the Calypso® System then provided real-time intrafractional target tracking. The NCI CTCAE v3.0 was used to assess urinary and rectal toxicity during treatment and at defined follow-up time points. Biochemical response and quality of life measurements were made at concurrent follow-up points.Results: Urinary toxicities were most common. At 6 months, 19.6, 2.9, and 4.9% of patients reported grades 1–2 urinary frequency, dysuria, and retention, respectively. Rectal toxicities were uncommon. By 12 months, 2.9% of patients reported painless rectal bleeding with subsequent symptom resolution without requiring invasive interventions. Quality of life measurements demonstrated a significant decline over baseline in urinary irritative/obstructive scores at 1 month following SABR but otherwise did not demonstrate any difference for bowel, bladder, and sexual function scores at any other follow-up time point. One patient suffered biochemical recurrence at 6 years following SABR.Conclusion: At 5 years, minimum follow-up for this favorable patient cohort, prostate SABR resulted in favorable toxicity, quality of life, and biochemical outcomes.

This paper focuses on the real-time target tracking for a Cartesian robot in 2 dimensions by using visual servo control. It is the process of controlling a system in servoloop by using visual image data as a referenced command to control the motion of a robot. The visual servo control can be divided into 2 loops. The first loop is image processing loop for tracking a target in video frame and the second loop is servo loop for feedback control of a Cartesian robot. A target tracking is based on color probability image using a Continuously Adaptive Mean Shift (CAMShift) algorithm that finds the most probable target position from color histogram in the current frame with a single stationary camera. The experimental results illustrate that a Cartesian robot is automatically follow a moving target and record it. The visual servo control can be adapted to more complex control applications.

Air–Ground Surveillance Sensor Network based on edge computing for target tracking

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

Radar systems play a crucial role in target tracking within urban environments, where challenges such as clutter, multipath effects, and electromagnetic interference significantly impact detection accuracy. Traditional radar methods often struggle to adapt to dynamic urban conditions, leading to decreased reliability in real-time target tracking. This study aims to develop and evaluate an AI-driven adaptive radar system that enhances tracking accuracy in urban settings. The research employs a quantitative approach using simulations to model radar signal processing under various environmental conditions. The AI model, based on Convolutional Neural Networks (CNN), is trained to optimize radar performance by filtering out noise and dynamically adjusting detection parameters. The results indicate that the AI-based radar system achieves a tracking accuracy of 95.2%, significantly outperforming traditional radar systems, which only reach 80% accuracy. Additionally, the AI-enhanced radar reduces response time to 120 milliseconds, compared to 250 milliseconds in conventional systems, demonstrating improved real-time processing capabilities. The system also exhibits greater resilience to high-clutter environments, maintaining stable target detection despite signal interference. These findings highlight the potential of AI in enhancing radar functionality for applications such as surveillance, traffic monitoring, and security. Future research should focus on integrating AI-driven radar with real-world radar hardware, exploring multi-sensor fusion, and refining adaptive learning techniques to further optimize tracking performance in complex environments

Image-free single-pixel detection, which has the advantages of low computing power requirements, a wide working waveband and high detection speed, provides a new paradigm for target tracking. A dedicated mask-based four-pixel parallel detection system is proposed for ultraviolet (UV) target positioning and tracking. The system consists of four parallel branches. Four masks with linearly increasing transmittance provide one-order geometric moment modulation of the scene simultaneously. The four branches are arranged in a diamond structure to ensure complementarity and symmetry in the x and y directions, respectively. A chrome mask and a photomultiplier tube module are selected to ensure the system performance in the UV waveband. Target positioning is achieved by centroid calculation with a geometric moment and a global system calibration method is proposed to ensure accuracy. The validity of the proposed system is demonstrated with tracking experiments using a UV target. This work provides a practical and low-cost solution for real-time UV target positioning and tracking.

A real-time visual servo tracking system for an industrial robot has been developed. The position sensitive detector or PSD, instead of the CCD, is used as a real time vision sensor due to its fast response (The position is converted to analog current). A neural network learns the complex association between the object position and its sensor reading and uses it to track that object. It also turns out that this scheme lends itself to a convenient way to teach a workpath for the robot. Furthermore, for real-time use of the neural net, a novel architecture has been developed based on the concept of input space partitioning and local learning. It exhibits characteristics of fast processing and learning as well as optimal usage of hidden neurons. >

Target tracking is an important research topic and key technology in visual surveillance, the theory is determined each frame in the Abstr act Image movement involved background movement and target movement.The complexities of movement increased the difficulty of target tracking.This study was implemented in the Window platform and the Microsoft visual C compiler environment, and used camera as a video capture device.The system of moving target detection and tracking was realized based on continuously adaptive mean shift and open computer vision theory, achieved moving target tracking by improving the algorithm.The experimental results showed that the proposed moving target detection algorithm was superior to the traditional mean shift tracking.It could track target efficiently, and further real-time drew motion trajectory.

This paper proposes a real-time visual hand tracking and posture estimation system to guide a robotic arm in gripping gestures. We present an original 26 degree-of-freedom model of the hand, for which forward and inverse kinematics formulations have been developed. The hand, wearing a dark glove marked with colored cues, is tracked in real time in the image while completing a grasping task. The position of the color markers in the image is used to reconstruct the hand's pose. Occlusions are handled by position prediction and validation. A robot gripper can then be guided by the different configurations of the hand with respect to the object to be grasped, provided the relationship between the hand and the gripper is known. Successful results for the teleoperated control of a robot manipulator in simulation are presented.