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In this study, an optimal adaptive control approach is established to solve the robust output tracking problem of a class of continuous time uncertain linear systems based on the policy iteration (PI) in actor-critic algorithm. First, by augmenting the integral variables of the tracking error into state variables, the robust tracking problem is transformed into a robust control problem of an augmented uncertain linear system. It is proven that the robust control law of the augmented system enables the output of the considered system to track a polynomial time signal asymptotically. Second, an optimal control method in the corresponding auxiliary nominal system is established, and based on the Bellman optimality principle, PI algorithms are proposed to solve online tracking controllers for the matched and the mismatched uncertain systems. Finally, for testing the availability of the proposed approach and theoretical results, two numerical experiments are provided. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel object tracking method based on sparse representation. First, we propose a relaxed sparse representation model, based on which the tracking problem is casted as an inverse sparse representation process. In this process, the target template is able to be sparsely approximated by all candidate samples. Second, we present an objective function that combines the sparse representation process of different fragments, the relaxed representation scheme and a weight reference prior. Based on some propositions, the proposed objective function can be solved by using an iteration algorithm. In addition, we design a tracking framework based on the proposed representation model and a simple online update manner. Finally, numerous experiments are conducted on some challenging sequences to compare our tracking method with some state-of-the-art ones. Both qualitative and quantitative results demonstrate that the proposed tracking method performs better than other competing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust visual tracking algorithm using online Max-Margin structural learning with sparse intersection kernel approximations to address the appearance variation, such as pose and scale variations, illumination changes, and occlusion. In the proposed tracking framework, we introduce a new sparse approximate HOG-based appearance model to represent the tracked object in high-dimensional feature space, which is efficient for measurement of non-linear intersection kernel. We then propose a optimisation method by extending Pegasos to the structural output with sparse intersection kernel in an online setting. Based on the new discriminative appearance models, we build a tracking algorithm using a Bayesian state inference framework to update the dynamic motion of object. The qualitative and quantitative experimental evaluations on 14 challenging video sequences demonstrate that the proposed tracking algorithm outperforms state-of-the-art trackers. We also show the low time complexity of the proposed tracker. [ABSTRACT FROM AUTHOR]

Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by in-plane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background clutter and local blur. In this paper, we present a novel tri-tracking framework combining different views (different models using independent features) for robust object tracking. This new tracking framework exploits a hybrid discriminative generative model based on online semi-supervised learning. We only need the first frame for parameters initialization, and then the tracking process is automatic in the remaining frames, with updating the model online to capture the changes of both object appearance and background. There are three main contributions in our tri-tracking approach. First, we propose a tracking framework for combining generative model and discriminative model, together with different cues that complement each other. Second, by introducing a third tracker, we provide a solution to the problem that it is difficult to combine two classification results in co-training framework when they are opposite. Third, we propose a principle way for combing different views, which based on their Discriminative power. We conduct experiments on some challenging videos, the results from which demonstrate that the proposed tri-tracking framework is robust. [ABSTRACT FROM AUTHOR]

The paper focuses on the implementation of a hybrid control technique applied to a pneumatically actuated micro robot which is modelled and simulated to operate in a constrained environment for an in-pipe application. A mathematical model that represents the dynamic characteristics of micro robot is first derived and later the dynamic response of the robot system subjected to different input excitations rigorously investigated. Three different types of control algorithms are designed and integrated into this system via a proportional-integral-derivative (PID) controller, sliding mode controller (SMC) and active force control (AFC) method. This hybrid scheme is to be known as AFC+SMC+PID and its primary objective is to ensure accurate and robust trajectory tracking control of the micro robot system is achieved. The performance of the control system under different types of disturbances is evaluated through a simulation study. The obtained results clearly demonstrate an effective trajectory tracking capability of the wormlike micro robot in spite of the negative effects of the external disturbances. [ABSTRACT FROM AUTHOR]

Tracking people or objects across multiple cameras and maintaining a track within a camera is a challenging task in applications such as video surveillance, traffic monitoring, event detection and recognition. Some of the major challenges while tracking a target are illumination/scale changes and partial occlusion. In this paper, we propose a novel tracking framework using particle filter to efficiently track an object within a camera and a blob-based target association scheme for tracking across cameras. The proposed particle filter tracking algorithm uses a fragment-based approach to model the target and track it by fusing color and gradient features. The proposed solution incorporates coarser level spatial information by fragmenting each particle and is shown to be useful for tracking under partial occlusion. A fast yet robust model update is employed to overcome illumination changes. Experimental results show (i) the robustness of the fragment-based tracking approach with respect to illumination/scale change and partial occlusion and (ii) tracking persons across two cameras. [ABSTRACT FROM AUTHOR]

• A novel robust multi-sensor multi-target tracking filter. • Estimate detection probability and clutter rate online. • Based on existing filters: the cardinalized probability hypothesis density (CPHD) filter and the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. • Perform well in tracking scenarios with bearing-only sensors. This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate target trajectories and robust Cardinalized Probability Hypothesis Density (CPHD) filters to estimate the clutter rates. The target detection probability is augmented to the filtering state space for joint estimation. Experimental results show that the proposed robust filter exhibits near-optimal performance in the sense that it is comparable to the optimal MS-GLMB operating with true clutter rate and detection probability. More importantly, it outperforms other studied filters when the detection profile and clutter rate are unknown and time-variant. This is attributed to the ability of the robust filter to learn the background parameters on-the-fly. [ABSTRACT FROM AUTHOR]

Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of "parts" such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function that exhibits a high degree of correlation with the tracking error. [ABSTRACT FROM AUTHOR]

This paper proposes a novel, observer-based, nonlinear control strategy aimed at facilitating the pursuit of nanomechanical measurements within the noisy environments of industrial production lines. The investigation makes use of a laboratory-prototype emulating an architecture composed by a sample to be analyzed, and by a metrology platform whose task is to carry out in-line measurements, while the whole system is being affected by exogenous vibrations. The control objective is to lock the distance between the metrology platform and the sample, thereby allowing the pursuit of the measurement task. The proposed solution achieves robustness with respect to unknown plant parameters and exogenous disturbances by means of a combination of high-frequency and high-gain tools, and by relying on a novel "learning observer". The proposed design is formally analyzed, and then experimentally validated by providing a comparative study with a PID-based control-strategy. The experimental results indicate, overall, a favorable performance of the proposed control algorithm over the PID counterpart. [ABSTRACT FROM AUTHOR]