Implementation of Tracking of a Moving Object Based on Camshift Approach with a UAV

Coşkun, Musab; Ünal, Sencer

doi:10.1016/j.protcy.2016.01.116

Cited by 15 publications

(9 citation statements)

References 1 publication

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“…The other popular tracker is mean shift that employs mean shift analysis to identify a target candidate region, which has most similar appearance to target model in terms of intensity distribution. Adaptive mean shift (Camshift) algorithm is popular OpenCV algorithm for tracking [37]. In [38] combined mean shift and kalman filter is used for tracking.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition and Tracking in Videos

Tathe¹,

Narote²,

Narote³

2017

Adv. sci. technol. eng. syst. j.

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Section: Related Workmentioning

confidence: 99%

Face Recognition and Tracking in Videos

Tathe¹,

Narote²,

Narote³

2017

Adv. sci. technol. eng. syst. j.

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“…‫̇ݕ‬௧ = ‫̇ݕ‬௧ ିଵ + ‫‪̇௧‬ݕܩ‬ ିଵ (0, ߪ ௬̇) (10) ‫ݓ‬ ௧ = ‫ݓ‬ ௧ିଵ + ‫ݓܩ‬ ௧ିଵ (0, ߪ ௪ ) (11) ℎ ௧ = ℎ ௧ିଵ + ‫ܩ‬ℎ ௧ିଵ (0, ߪ ) (12) Where G * ( * ) is zero mean white Gaussian noise components for several unidentified dynamic factors that may happened during tracking (sudden random motion, slight acceleration, …). σ * is the variance of G * ( * ).…”

Section: Dynamic Model Updatementioning

confidence: 99%

“…The equations (11,12) are updated by inserting the effect of scale change represented by a and d components in the ego motion transformation matrix E as the following:…”

Section: Dynamic Model Updatementioning

confidence: 99%

“…The fastabrupt motion, low resolution, noisy imagery, cluttered background, low contrast, and small target size are common issues that should be taken into considerations [1,2]. There are also many factors govern the selection of the suitable object tracker such as the environment nature, object scale and types of the object motion [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Optimized UAV object tracking framework based on Integrated Particle filter with ego-motion transformation matrix

et al. 2017

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Vision based object tracking problem still a hot and important area of research specially when the tracking algorithms are performed by the aircraft unmanned vehicle (UAV). Tracking with the UAV requires special considerations due to the flight maneuvers, environmental conditions and aircraft moving camera. The ego motion calculations can compensate the effect of the moving background resulted from the moving camera. In this paper an optimized object tracking framework is introduced to tackle this problem based on particle filter. It integrates the calculated ego motion transformation matrix with the dynamic model of the particle filter during the prediction stage. Then apply the correction stage on the particle filter observation model which based on two kinds of features includes Haar-like Rectangles and edge orientation histogram (EOH) features. The Gentle AdaBoost classifier is used to select the most informative features as a preliminary step. The experimental results achieved more than 94.6% rate of successful tracking during different scenarios of the VIVID database in real time tracking speed.

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“…Target model and tracking algorithm are two key factors directly affecting the tracking performance. The hue histogram model which has the advantages of a simple expression, less computation, and so on, has been used in many tracking methods, such as Meanshift and Camshift [7–10]. However, because the hue histogram model describes the target characteristics by the statistical information, it would cause losing some detailed information of the target.…”

Section: Introductionmentioning

confidence: 99%