Video object tracking using adaptive Kalman filter

Weng, Shiuh-Ku; Kuo, Chung-Ming; Tu, Shu-Kang

doi:10.1016/j.jvcir.2006.03.004

Cited by 288 publications

(118 citation statements)

References 12 publications

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“…To achieve such segmentation, clustering approaches have been used in previous works [21,22]. Weng et al proposed a combination of k-means clustering and extended Kalman filters for object tracking [23].…”

Section: Related Workmentioning

confidence: 99%

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Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

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Original scientific paper In this paper a new method based on evolutionary clustering and locally linear neuro-fuzzy (LLNF) models is proposed for the problem of object tracking in videos. This approach utilizes clustering on color feature space to obtain a model of object which is given at the initial frame. To achieve the optimal clustering, evolutionary optimization methods are used. Based on the results of clustering, parameters of LLNF model is determined so it can be used as an identifier of object during the real time video streaming. To track the object, a swarm of weighted evolving linear models are used to estimate the location and size of the object at next frame based on its current and previous states. The performance of the proposed method is evaluated on a benchmark data set and compared to other methods performed on the same data set. The results show that the accuracy of the proposed method is superior to previous methods. Keywords: clustering; evolutionary computation; Locally Linear Neuro-Fuzzy model; object tracking; swarm optimization Praćenje predmeta u video snimkama pomoću modela evolucijskog grupiranja i lokalno linearnih neuro-fuzzy modelaIzvorni znanstveni članak U radu se za problem praćenja objekta u video snimkama predlaže nova metoda koja se zasniva na modelima evolucijskog grupiranja i lokalno linearnim neuro-fuzzy modelima (LLNF). Taj pristup koristi grupiranje na prostoru karakteristike u boji kako bi se dobio model predmeta zadanog u početnom okviru. Za postizanje optimalnog grupiranja, primijenjene su metode evolucijske optimizacije. Na temelju rezultata grupiranja određeni su parametri LLNF modela te se on može rabiti kao identifikator objekta tijekom video prijenosa u realnom vremenu. Za praćenje objekta koristi se roj ponderiranih izvedenih linearnih modela za procjenu lokacije i veličine objekta u sljedećem okviru na temelju njegovog postojećeg i prethodnih stanja. Učinkovitost predložene metode procijenjena je na referentnom nizu podataka i uspoređena s drugim metodama provedenim na istom nizu podataka. Rezultati pokazuju da je po točnosti predložena metoda bolja od prethodnih.

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Section: Related Workmentioning

confidence: 99%

“…The most famous one of clustering methods is k-means algorithm [23]. This algorithm is fast and useful to many problems, but it has several drawbacks.…”

Section: Clustering Of Samplesmentioning

confidence: 99%

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

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“…In this work, we used the adaptive Kalman filter that was developed by Shiuh-Ku et al [23]. The proposed Kalman filter estimates motion information extracted by feature tracking.…”

Section: Adaptive Extended Kalman Filtermentioning

confidence: 99%

“…The outcome of the LK algorithm is used as the measurement for the correction step. In the consecutive frames, the time interval is very short, then velocity is assumed uniform and used instead of position in the model [23]. Therefore, we can model the displacement vector in time intervals as (18).…”

Section: Adaptive Extended Kalman Filtermentioning

confidence: 99%

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Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Oskoei

2017

Robotics

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This paper proposes an adaptive Kalman filter (AKF) to improve the performance of a vision-based human machine interface (HMI) applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.

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Vision‐Based Tracking and Position Estimation of Moving Targets for Unmanned Helicopter Systems

Lin

Hsiao

2013

Asian Journal of Control

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The primary goal of this study is to track a ground-moving target using a machine-vision system installed on an unmanned helicopter, and to estimate its position if the target becomes unobservable. The machine-vision system is accomplished using real-time color images obtained from a charge-coupled device (CCD) camera mounted on a computer-controlled gimbaled system that can pitch and yaw. To avoid real-time image-tracking failure resulting from a moving target becoming concealed, the Kalman filtering technique is applied to predict the target's follow-on position, so that the camera can continuously track the target. The entire system is initially tested on the ground and then mounted on a helicopter for in-flight testing. The following three cases are shown in the flight tests: (1) an uncovered static target; (2) a moving visible target; and (3) a target that moves in a straight line at a constant speed and becomes temporarily concealed. The vision-based tracking system with the developed algorithm is successfully applied in all three cases.

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Video object tracking using adaptive Kalman filter

Cited by 288 publications

References 12 publications

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Vision‐Based Tracking and Position Estimation of Moving Targets for Unmanned Helicopter Systems

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