Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

Choi, In Hwan; Pak, Jung Min; Ahn, Choon Ki; Lee, Seung-Han; Lim, Myo Taeg; Song, Moon Kyou

doi:10.1016/j.measurement.2015.07.020

Cited by 27 publications

(8 citation statements)

References 42 publications

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“…Choi et al exploited an arbitration algorithm between a finite impulse response (FIR) filter and optical flow by adaptive neuro-fuzzy inference system for tracking research. 26 And there are also many tracking algorithms 27 that focus on appearance and motion model definition to deal with the complex scene and avoid drifting.…”

Section: Related Workmentioning

confidence: 99%

Visual tracking via improving motion model and model updater

Xue

Feng

et al. 2018

International Journal of Advanced Robotic Systems

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Motion model and model updater are two necessary components for online visual tracking. On the one hand, an effective motion model needs to strike the right balance between target processing, to account for the target appearance and scene analysis, and to describe stable background information. Most conventional trackers focus on one aspect out of the two and hence are not able to achieve the correct balance. On the other hand, the admirable model update needs to consider both the tracking speed and the model drift. Most tracking models are updated on every frame or fixed frames, so it cannot achieve the best performance. In this article, we solve the motion model problem by collaboratively using salient region detection and image segmentation. Particularly, the two methods are for different purposes. In the absence of prior knowledge, the former considers image attributes like color, gradient, edges, and boundaries then forms a robust object; the latter aggregates individual pixels into meaningful atomic regions by using the prior knowledge of target and background in the video sequence. Taking advantage of their complementary roles, we construct a more reasonable confidence map. For model update problems, we dynamically update the model by analyzing scene with image similarity, which not only reduces the update frequency of the model but also suppresses the model drift. Finally, we use these improved building blocks not only to do comparative tests but also to give a basic tracker, and extensive experimental results on OTB50 show that the proposed methods perform favorably against the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Visual tracking via improving motion model and model updater

Xue

Feng

et al. 2018

International Journal of Advanced Robotic Systems

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“…It overcomes the limitations of fuzzy inference that the identification procedure of the parameters in membership functions (MFs) is not efficient for complex systems. Recently, ANFIS has been widely used in maximum power point tracking (MPPT) [20], face recognition [21] and object tracking [22].…”

Section: Anfismentioning

confidence: 99%

ANFIS-Based Modeling for Photovoltaic Characteristics Estimation

Pan

et al. 2016

Symmetry

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Due to the high cost of photovoltaic (PV) modules, an accurate performance estimation method is significantly valuable for studying the electrical characteristics of PV generation systems. Conventional analytical PV models are usually composed by nonlinear exponential functions and a good number of unknown parameters must be identified before using. In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based modeling method is proposed to predict the current-voltage characteristics of PV modules. The effectiveness of the proposed modeling method is evaluated through comparison with Villalva's model, radial basis function neural networks (RBFNN) based model and support vector regression (SVR) based model. Simulation and experimental results confirm both the feasibility and the effectiveness of the proposed method.

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“…A state estimator with FIR structure (i.e., FIR filter) was proposed in [13]. Various designs of FIR filters can be found in [6], [7], and [13]- [29]. The above FIR filters have exhibited better robustness against model errors and incorrect noise information compared with the KF.…”

Section: Introductionmentioning

confidence: 99%

Switching Extensible FIR Filter Bank for Adaptive Horizon State Estimation With Application

Pak

Ahn

Shmaliy

et al. 2016

IEEE Trans. Contr. Syst. Technol.

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Horizon size is an important parameter that affects the estimation performance of finite impulse response (FIR) filters. In this brief, we propose a novel adaptive horizon approach that aims to adapt the horizon size at each time point. The approach suggests providing state estimation using a bank of FIR filters called the switching extensible FIR filter bank (SEFFB), which consists of several FIR filters operating using different horizon sizes. The horizon sizes and the number of FIR filters in the SEFFB are adapted to changes in system characteristics using maximum likelihood. The SEFFB is applied to target tracking using a ground moving target indicator. A significant performance improvement is demonstrated using the SEFFB in comparison with a single FIR filter using constant optimal horizon size.Index Terms-Adaptive horizon, finite impulse response (FIR) filter, state estimation, switching extensible FIR filter bank (SEFFB).

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Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

Cited by 27 publications

References 42 publications

Visual tracking via improving motion model and model updater

Visual tracking via improving motion model and model updater

ANFIS-Based Modeling for Photovoltaic Characteristics Estimation

Switching Extensible FIR Filter Bank for Adaptive Horizon State Estimation With Application

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