Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

Cao, Y.; Wang, Gang; Yan, Dongmei; Zhao, Zhongming

doi:10.3390/rs8010028

Cited by 47 publications

(43 citation statements)

References 33 publications

(47 reference statements)

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“…Mean shift target tracking (MS) [3] gives estimation of the position based on probability density and sampling mean. In MSDU, mean shift theory is used to move the target candidate to the location most similar to that of the target model which is new location.…”

Section: Mean Shift Algorithmmentioning

confidence: 99%

“…This damage was controlled by the real-time target information. For the collection of real-time target information, Target Tracking Based on Detection Updates (MSDU) [3] draws lessons from Tracking Learning Detection (TLD) [3] and uses the real-time target detection results as a priori knowledge. To be specific, the detection results give realtime information about the target, and consequently, if there is an obvious difference between the detection results and the tracking results, the tracking results are probably not believable.…”

Section: Detection Of Target Modelmentioning

confidence: 99%

“…Effective tracking evolution criterion: This criteria was evaluated by taking similarity in the spatial domain, δ, as measure. The δ between the detection result and its nearest-neighbour matched tracking prediction is expressed as the Euclidean distance [3] between their centers:…”

Section: Detection Of Target Modelmentioning

confidence: 99%

See 2 more Smart Citations

EECLA: Clustering and Localization Techniques to Improve Energy Efficient Routing in Vehicle Tracking using Wireless Sensor Networks

Gummadi¹,

Rao²

2018

IJET

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The applications of wireless sensor networks became more usable in daily life. In spite of many proposed techniques and methods, energy efficient routing in WSN is still an open issue. In this paper we made an attempt to give one of the solution for this problem in vehicle tracking system based on the vehicle sensor nodes. We studied many existing works, were failed in handling location and energy efficient routing of vehicle tracking properly. We proposed an algorithm which handles clustering and location at time and improves the performance of the system. This algorithm uses the fundamentals of LEACH, CLAEER and mean shifted algorithm. We conducted a sequence of experiments and our algorithm EECLA (Clustering and Localization Techniques to Improve Energy Efficient Routing in Wireless Sensor Networks) has given better results than the existed one with more accuracy.

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Section: Mean Shift Algorithmmentioning

confidence: 99%

Section: Detection Of Target Modelmentioning

confidence: 99%

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EECLA: Clustering and Localization Techniques to Improve Energy Efficient Routing in Vehicle Tracking using Wireless Sensor Networks

Gummadi¹,

Rao²

2018

IJET

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“…Calculate the mixing probability u ij,k−1 according to Equation (3). Next, determine the Interaction by using CP.…”

Section: Implementation Steps Of the Cpimm-naukf Algorithmmentioning

confidence: 99%

“…With the continuous development of space technology, maneuvering ability in real time is a key factor in completing complicated space missions for maneuvering targets [1][2][3][4]. Performing accurate maneuvering target tracking and precise locating are the core issues in the field of space target surveillance [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

et al. 2017

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Featured Application: The work is very effective and applicable in the fields of the maneuvering target tracking and navigations system design.Abstract: Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

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Infrared target tracking based on proximal robust principal component analysis method

Wan

et al. 2021

Appl Intell

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Infrared target tracking plays an important role in both civil and military fields. The main challenges in designing a robust and high-precision tracker for infrared sequences include overlap, occlusion and appearance change. To this end, this paper proposes an infrared target tracker based on proximal robust principal component analysis method. Firstly, the observation matrix is decomposed into a sparse occlusion matrix and a low-rank target matrix, and the constraint optimization is carried out with an approaching proximal norm which is better than 𝑙 1 norm. To solve this convex optimization problem, Alternating Direction Method of Multipliers (ADMM) is employed to estimate the variables alternately. Finally, the framework of particle filter with model update strategy is exploited to locate the target. Through a series of experiments on real infrared target sequences, the effectiveness and robustness of our algorithm are proved.

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Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

Cited by 47 publications

References 33 publications

EECLA: Clustering and Localization Techniques to Improve Energy Efficient Routing in Vehicle Tracking using Wireless Sensor Networks

EECLA: Clustering and Localization Techniques to Improve Energy Efficient Routing in Vehicle Tracking using Wireless Sensor Networks

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

Infrared target tracking based on proximal robust principal component analysis method

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