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Robust complete occlusion handling in adaptive template matching target tracking

ELECTRONICS LETTERS 2012年第14期48卷 831-U48页

作者： Di Caterina, G. Soraghan, J. J. Univ Strathclyde Dept Elect & Elect Engn CeSIP Glasgow G1 1XW Lanark Scotland

Occlusion is a common problem in visual tracking applications. In adaptive template matching target tracking, characteristics of the occluding object may be erroneously incorporated in the new updated template. Such a corrupted template affects the tracker robustness and may lead to target loss. A novel technique is proposed for complete occlusion handling, in the context of a SWAD-based adaptive template matching tracker. A matrix of pixel weights is adopted, so separate parts of the template are updated at different rates and the overall structure of the target template is significantly preserved. Experimental results show that such a technique improves the robustness of the tracking algorithm in occlusion scenarios, for a successful recovery once the target becomes visible again.

关键词： Robust control Pixels tracking algorithms Light filters Infinite impulse response filters

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tracking Maneuvering Group Target with Extension Predicted and Best Model Augmentation Method Adapted

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-9页

作者： Gan, Linhai Wang, Gang Air Force Engn Univ Air & Missile Def Coll Xian Shaanxi Peoples R China

The random matrix (RM) method is widely applied for group target tracking. The assumption that the group extension keeps invariant in conventional RM method is not yet valid, as the orientation of the group varies rapidly while it is maneuvering;thus, a new approach with group extension predicted is derived here. To match the group maneuvering, a best model augmentation (BMA) method is introduced. The existing BMA method uses a fixed basic model set, which may lead to a poor performance when it could not ensure basic coverage of true motion modes. Here, a maneuvering group target tracking algorithm is proposed, where the group extension prediction and the BMA adaption are exploited. The performance of the proposed algorithm will be illustrated by simulation.

关键词： MANEUVERING boards RANDOM matrices tracking algorithms SIMULATION methods & models PREDICTION models

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Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed tracking Performance

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Zhu Guoqiang Liu Jinkun Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries;(2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity;(3) only one parameter needs to be updated online at each design step, which significantly reduces the computational burden. It is proved that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

关键词： NEURAL networks (Computer science) ADAPTIVE control systems HYPERSONIC flow tracking algorithms CONSTRAINTS (Linguistics) SIMULATION methods & models

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Depth space partitioning for omni-stereo object tracking

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IET COMPUTER VISION 2012年第2期6卷 153-163页

作者： Xiong, Z-H Cheng, I. Chen, W. Basu, A. Zhang, M-J Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Univ Alberta Edmonton AB T6G 2E8 Canada

Using stereo disparity or depth information to detect and track moving objects is receiving increasing attention in recent years. However, this approach suffers from some difficulties, such as synchronisation between two cameras and doubling of the image-data size. Besides, traditional stereo-imaging systems have a limited field of view (FOV), which means that they need to rotate the cameras when an object moves out of view. In this research, the authors present a depth-space partitioning algorithm for performing object tracking using single-camera omni-stereo imaging system. The proposed method uses a catadioptric omnidirectional stereo-imaging system to capture omni-stereo image 'pairs.' This imaging system has 3608 FOV, avoiding the need for rotating cameras when tracking a moving object. In order to estimate omni-stereo disparity, the authors present a depth-space partitioning strategy. It partitions three-dimensional depth space with a series of co-axial cylinders, models the disparity estimation as a pixel-labelling problem and establishes an energy minimisation function for solving this problem using graph cuts optimisation. Based on the omni-stereo disparity-estimation results, the authors detect and track-moving objects based on omni-stereo disparity motion vector, which is the difference between two consecutive disparity maps. Experiments on moving car tracking justify the proposed method.

关键词： DEPTH profiling INFORMATION processing SYNCHRONIZATION STEREOSCOPIC cameras DATA analysis CATADIOPTRIC systems tracking algorithms

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Computer-aided checking systems for the geometric parameters of objects based on wide-range tracking fluidic sensors

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AUTOMATION AND REMOTE CONTROL 2013年第11期74卷 1903-1911页

作者： Sergeev, A. S. Lesnoi, B. V. Volgograd State Tech Univ Volgograd Russia

This paper demonstrates the efficiency of wide-range tracking fluidic sensors in computer-aided checking systems. The functional models of air sensors are studied depending on influencing factors. The results obtained allow estimating transient processes, amplitude errors and phase errors, as well as metrological characteristics of tracking air sensors in computer-aided checking systems.

关键词： COMPUTER-aided design GEOMETRIC analysis PARAMETER estimation FLUIDIC devices tracking algorithms MATHEMATICAL models

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Online multi-person tracking with two-stage data association and online appearance model learning

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IET COMPUTER VISION 2017年第1期11卷 87-95页

作者： Ju, Jaeyong Kim, Daehun Ku, Bonhwa Han, David K. Ko, Hanseok Korea Univ Sch Elect Engn Seoul South Korea Off Naval Res Arlington VA USA

This study addresses the automatic multi-person tracking problem in complex scenes from a single, static, uncalibrated camera. In contrast with offline tracking approaches, a novel online multi-person tracking method is proposed based on a sequential tracking-by-detection framework, which can be applied to real-time applications. A two-stage data association is first developed to handle the drifting targets stemming from occlusions and people's abrupt motion changes. Subsequently, a novel online appearance learning is developed by using the incremental/ decremental support vector machine with an adaptive training sample collection strategy to ensure reliable data association and rapid learning. Experimental results show the effectiveness and robustness of the proposed method while demonstrating its compatibility with real-time applications.

关键词： tracking algorithms CAMERAS REAL-time computing SUPPORT vector machines COMPUTER vision

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Online visual tracking based on subspace representation with continuous occlusion modeling

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MULTIMEDIA SYSTEMS 2017年第3期23卷 357-368页

作者： Bo, Chunjuan Zhang, Junxing Liu, Changhong Yao, Qiang Dalian Nationalities Univ Coll Electromech Engn Dalian 116600 Peoples R China Dalian Nationalities Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China

As an important issue in image processing and computer vision, online visual tracking acts a critical role in numerous lines of research and has many potential applications. This paper presents a novel tracking algorithm based on subspace representation with continuous occlusion handling, the contributions of which are threefolds. First, this paper develops an effective objective function to represent the tracked object, in which the object reconstruction, the sparsity of the error term and the spatial consistency of the error term are simultaneously considered. Then, we derive an iterative algorithm to solve the proposed objective function based on the accelerated proximal gradient framework, and therefore obtain the optimal representation coefficients and the possible occlusion conditions. Finally, based on the proposed representation model, we design an effective likelihood function and a simple model update scheme for building a robust tracker within the particle filter framework. We conduct many experiments to evaluate the proposed tracking algorithm in comparisons with other state-of-the-art trackers. Both qualitative and quantitative evaluations demonstrate the proposed tracker achieves good performance.

关键词： SUBSPACE identification (Mathematics) COMPUTER vision MULTIPLE correspondence analysis (Statistics) tracking algorithms IMAGE processing -- Mathematical models

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Visual tracking Based on Complementary Learners with Distractor Handling

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-13页

作者： Wibowo, Suryo Adhi Lee, Hansoo Kim, Eun Kyeong Kim, Sungshin Pusan Natl Univ Dept Elect & Comp Engn Busan South Korea

The representation of the object is an important factor in building a robust visual object tracking algorithm. To resolve this problem, complementary learners that use color histogram-and correlation filter-based representation to represent the target object can be used since they each have advantages that can be exploited to compensate the other's drawback in visual tracking. Further, a tracking algorithm can fail because of the distractor, even when complementary learners have been implemented for the target object representation. In this study, we show that, in order to handle the distractor, first the distractor must be detected by learning the responses fromthe color-histogram-and correlation-filter-based representation. Then, to determine the target location, we can decide whether the responses from each representation should be merged or only the response from the correlation filter should be used. This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets. It was verified that our proposed method performs favorably as compared with several state-of-the-art visual tracking algorithms.

关键词： OBJECT tracking (Computer vision) HISTOGRAMS tracking algorithms CORRELATION (Statistics) DATA analysis

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Distributed tracking of heterogeneous nonlinear fractional-order multi-agent systems with an unknown leader

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2017年第5期354卷 2226-2244页

作者： Gong, Ping Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China

By applying the fractional Lyapunov direct method, this paper investigates the distributed tracking problem of nonlinear fractional-order multi-agent systems subject to heterogeneous control gains and a time-varying leader whose input is unknown and bounded over a general directed graph. Due to the existence of heterogeneous control gains as well as a time-varying unknown leader in the nonlinear systems, the fractional-order dynamics of each agent is in essence heterogeneous. At first, a discontinuous distributed controller is constructed to guarantee that the distributed tracking control problem can be solved if some conditions are satisfied. Next, a continuous distributed controller is further proposed to eliminate the undesirable chattering phenomenon of the discontinuous controller, where the upper bound of the tracking error is uniformly bounded and can be made small enough by choosing the parameters appropriately. Finally, some simulation examples are presented to verify the effectiveness of the main results. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： NONLINEAR functions MULTIAGENT systems tracking algorithms PROBLEM solving TIME-varying systems

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tracking algorithm with radar and infrared sensors using a novel adaptive grid interacting multiple model

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2014年第5期8卷 270-276页

作者： Wu, Panlong Li, Xingxiu Zhang, Lianzheng Bo, Yuming Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China

This study presents a novel adaptive grid interacting multiple model based on modified iterated extended Kalman filter (AGIMM-MIEKF) for tracking a manoeuvreing target using radar/infrared (IR) heterogeneous sensors. This tracking algorithm is developed by aligning observation data of radar/IR sensors in time, and fusing the synthesised data before applying to AGIMM-MIEKF algorithm. Under the architecture of the proposed algorithm, the AGIMM deals with the model switching, whereas the MIEKF accounts for non-linearity in the dynamic system models. A new measurement update equation and an iterated termination criterion are derived and applied to radar/IR tracking system. The simulation results show that the presented AGIMM-MIEKF has higher tracking precision than the traditional algorithms.

关键词： tracking algorithms INFRARED detectors ELECTRON tubes -- Grids ITERATIVE methods (Mathematics) KALMAN filtering SIMULATION methods & models

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