Dynamic Visual Tracking Research Articles

Adaptive Neural-PID Visual Servoing Tracking Control via Extreme Learning Machine

The vision-guided robot is intensively embedded in modern industry, but it is still a challenge to track moving objects in real time accurately. In this paper, a hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) is proposed for dynamic visual tracking of the manipulator. The scheme extracts line features on the image plane based on a laser-camera system and determines an optimal control input to guide the robot, so that the image features are aligned with their desired positions. The observation and state–space equations are first determined by analyzing the motion features of the camera and the object. The system is then represented as an autoregressive moving average with extra input (ARMAX) and a valid estimation model. The adaptive predictor estimates online the relevant 3D parameters between the camera and the object, which are subsequently used to calculate the system sensitivity of the neural network. The ELM–PID controller is designed for adaptive adjustment of control parameters, and the scheme was validated on a physical robot platform. The experimental results showed that the proposed method’s vision-tracking control displayed superior performance to pure P and PID controllers.

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator.

動到哪 追到哪－動態視覺追蹤系統

動到哪 追到哪－動態視覺追蹤系統

Tracking feature extraction based on manifold learning framework

Manifold learning is a fast growing area of research recently. The main purpose of manifold learning is to search for intrinsic variables underlying high-dimensional inputs which lie on or are close to a low-dimensional manifold. Different from current theoretical works and applications of manifold learning approaches, in our work manifold learning framework is transferred to tracking feature extraction for the first time. The contributions of this article include three aspects. Firstly, in this article, we focus on tracking feature extraction for dynamic visual tracking on dynamic systems. The feature extracted in this article is based on manifold learning framework and is particular for dynamic tracking purpose. It can be directly applied to system control of dynamic systems. This is different from most traditional tracking features which are used for recognition and detection. Secondly, the proposed tracking feature extraction method has been successfully applied to three different dynamic systems: dynamic robot system, intelligent vehicle system and aircraft visual navigation system. Thirdly, experimental results have proven the validity of the tracking method based on manifold learning framework. Particularly, in the tracking experiments the vision system is dynamic. The tracking method is also compared with the well-known mean-shift tracking method, and tracking results have shown that our method outperforms the latter.

Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking

Manifold learning is a hot topic in the field of computer science, particularly since nonlinear dimensionality reduction based on manifold learning was proposed in Science in 2000. The work has achieved great success. The main purpose of current manifold-learning approaches is to search for independent intrinsic variables underlying high dimensional inputs which lie on a low dimensional manifold. In this paper, a new manifold is built up in the training step of the process, on which the input training samples are set to be close to each other if the values of their intrinsic variables are close to each other. Then, the process of dimensionality reduction is transformed into a procedure of preserving the continuity of the intrinsic variables. By utilizing the new manifold, the dynamic tracking of a human who can move and rotate freely is achieved. From the theoretical point of view, it is the first approach to transfer the manifold-learning framework to dynamic tracking. From the application point of view, a new and low dimensional feature for visual tracking is obtained and successfully applied to the real-time tracking of a free-moving object from a dynamic vision system. Experimental results from a dynamic tracking system which is mounted on a dynamic robot validate the effectiveness of the new algorithm.

Dynamic visual tracking control of a mobile robot with image noise and occlusion robustness

This paper presents a robust visual tracking control design for a nonholonomic mobile robot equipped with a tilt camera. This design aims to allow the mobile robot to keep track of a dynamic moving target in the camera’s field-of-view; even though the target is temporarily fully occluded. To achieve this, a control system consisting of a visual tracking controller (VTC) and a visual state estimator (VSE) is proposed. A novel visual interaction model is derived to facilitate the design of VTC and VSE. The VSE is responsible for estimating the optimal target state and target image velocity in the image space. The VTC then calculates the corresponding command velocities for the mobile robot to work in the world coordinates. The proposed VSE not only possesses robustness against the image noise, but also overcomes the temporary occlusion problem. Computer simulations and practical experiments of a mobile robot to track a moving target have been carried out to validate the performance and robustness of the proposed system.

Dynamic Visual Tracking Using SDG-Like Matching

Motion detection/estimation plays a crucial role in dynamic visual tracking. Whether a dynamic visual tracking system can successfully track a moving target closely depends on the quality of motion detection/estimation results. In dynamic visual tracking, the camera used to capture images is not stationary, so any slight vibration of the camera motion or the target motion can lead to a blurry image causing the visual tracking performance to be deteriorated. To cope with this difficulty, a motion detection/estimation approach consisting of a region-based spatial distribution of Gaussians (SDG)-like matching algorithm, a template-update-with-memory algorithm, and a template mask is developed in this study. Moreover, linear interpolation on vision commands is performed to improve the tracking performance. A dynamic visual tracking system designed for locking the target's image in the center of the image plane is used as the test platform. Experimental results demonstrate the effectiveness of the proposed approach.

Dynamic Visual Tracking for Manipulators Using an Uncalibrated Fixed Camera

This paper presents a new controller for controlling a number of feature points on a robot manipulator to trace desired trajectories specified on the image plane of a fixed camera. It is assumed that the intrinsic and extrinsic parameters of the camera are not calibrated. A new adaptive algorithm is developed to estimate the unknown parameters online, based on three original ideas. First, we use the pseudoinverse of the depth-independent interaction matrix to map the image errors onto the joint space of the manipulator. By eliminating the depths in the interaction matrix, we can linearly parameterize the closed-loop dynamics of the manipulator. Second, to guarantee the existence of the pseudoinverse, the adaptive algorithm introduces a potential force to drive the estimated parameters away from the values that result in a singular Jacobian matrix. Third, to ensure that the estimated parameters are convergent to their true values up to a scale, we combine the Slotine-Li method with an online algorithm for minimizing the error between the estimated projections and real image coordinates of the feature points. We have proved asymptotic convergence of the image errors to zero by the Lyapunov theory based on the nonlinear robot dynamics. Experiments have been carried out to verify the performance of the proposed controller.

Dynamic visual tracking based on multiple feature matching and g–h filter

The area-based matching approach has been used extensively in many dynamic visual tracking systems to detect moving targets because it is computation efficient and does not require an object model. Unfortunately, area-based matching is sensitive to occlusion and illumination variation. In order to improve the robustness of visual tracking, two image cues, i.e., target template and target contour, are used in the proposed visual tracking algorithm. In particular, the target contour is represented by the active contour model that is used in combination with the fast greedy algorithm. However, to use the conventional active contour method, the initial contour needs to be provided manually. In order to facilitate the use of contour matching, a new approach that combines the adaptive background subtraction method with the border tracing technique was developed and is used to automatically generate the initial contour. In addition, a g–h filter is added to the visual loop to deal with the latency problem of visual feedback so that the performance of dynamic visual tracking can be improved. Experimental results demonstrate the effectiveness of the proposed approach.

Multirate predictor control scheme for visual servo control

An attractive control scheme for a position-based dynamic eye-in-hand visual servo system is proposed. The multi-rate predictor control scheme is used to overcome the inherent delay in the vision system and the multi-rate nature of the dynamic look-and-move visual servo control scheme. The control scheme takes the form of a simple predictor placed in the major feedback of the standard Smith predictor control scheme. This in turn eliminates the delay time between the dynamics of the observed target and the control, in a similar fashion to the feedforward Smith predictor. Hence the proposed control scheme is also known as the modified Smith predictor (MSP) control scheme. A detailed analysis of the proposed control scheme is presented for visual servo control. Representative computer simulation studies are presented to verify the effectiveness of the proposed multi-rate predictor control scheme for dynamic visual tracking.