Adaptive scale object tracking with kernelized correlation filters

Lei, 张雷 Zhang; Yan-jie, 王延杰 Wang; Hong-hai, 孙宏海 Sun; Zhi-jun, 姚志军 Yao; Pei, 吴培 Wu

doi:10.3788/ope.20162402.0448

Cited by 13 publications

(5 citation statements)

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“…Therefore, the kernel size can be dynamically adjusted using a function. The calculation method for the convolutional kernel is as Equation (10).…”

Section: Feature Fusion Network and Channel Attention Mechanismsmentioning

confidence: 99%

“…The estimated alignment errors and the attitude information output by the visual navigation system are used to correct the inertial navigation attitude. In [10], YOLOv3 is used to detect the runway region of interest (ROI), and an RDLines algorithm is employed to extract the left and right runway lines from the ROI. A visual/inertial combined navigation model is then designed within the framework of square-root unscented Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

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Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms

Liu,

Xue,

et al. 2024

Drones

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Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on YOLOX is designed for airport runway localization. To meet the requirements of model accuracy and inference speed for the landing guidance system, regression loss functions, probability prediction loss functions, activation functions, and feature extraction networks are designed. Secondly, a deep-learning-based runway line detection algorithm including feature extraction, classification prediction and segmentation networks is designed. To create an effective detection network, we propose efficient loss function and network evaluation methods Finally, a visual/inertial navigation system is established based on constant deformation for visual localization. The relative positioning results are fused and optimized with Kalman filter algorithms. Simulation and flight experiments demonstrate that the proposed algorithm exhibits significant advantages in terms of localization accuracy, real-time performance, and generalization ability, and can provide accurate positioning information during UAV landing processes.

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“…Therefore, the kernel size can be dynamically adjusted using a function. The calculation method for the convolutional kernel is as Equation (10).…”

Section: Feature Fusion Network and Channel Attention Mechanismsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms

Liu,

Xue,

et al. 2024

Drones

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show abstract

“…The computer used in the experiment is as follows: CPU is Intel, Core, i5, main frequency 2.0GHz, memory 4GB. In the experiment, the parameters of the KCF algorithm remain unchanged, and n is taken as the 50 in formula (7).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The correlation filter tracking method [3][4][5][6][7][8] is currently a popular research direction. Henriques 9 proposed the tracking cycle structure detection (CSK) algorithm, the algorithm using the cyclic matrix can be diagonalized Fourier properties will calculate classifier training and target detection is converted to the frequency domain on the fast calculation.…”

Section: Introductionmentioning

confidence: 99%

Research on Target Loss Warning Method of KCF Algorithm Based on Outlier Detection

Liu¹,

He²,

Zhao³

et al. 2018

dtetr

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Aiming at the problem that the KCF algorithm cannot detect the loss of targets, this paper proposes a target loss warning mechanism based on the outlier detection method. This method detects the peak value of the response of each frame. If the abnormal peak value is found, it can make a judgement that the target is lost or is about to be lost, and the problem that target loss cannot be detected is solved. Experimental results show that the method proposed in this paper can correctly warn when the target in the KCF algorithm tracking process is lost, the success rate reached 100%, It has high reliability and provide a reliable basis for loading target re detection and location mechanism after the target is lost.

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“…In order to facilitate quantitative analysis, three performance evaluation indicators are used in this paper: Center Location Error (CLE), Distance Precision (DP) and Overlap Precision (OP). Where CLE represents the average Euclidean distance between the detected target center position and the target true center position [15]; DP represents the ratio of the number of frames whose CLE is less than a certain threshold (20 pixels in the experiment) to the total number of frames of the video; OP indicates the ratio of the number of frames in which the overlap of the tracking frame exceeds a threshold (0.5 in the experiment) to the total number of frames of the video. The average CLE, DP, and OP of the six algorithms on the eight sets of test videos are shown in Table 1, Table 2, and Table 3.…”

Section: Experimental Environment and Evaluation Indicatorsmentioning

confidence: 99%

Scale Adaptive Kernel Correlation Filter Tracking Algorithm Combined with Learning Rate Adjustment

2018

MATEC Web Conf.

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Aiming at the problem that the traditional correlation filter tracking algorithm is prone to tracking failure under the target’s scale change and occlusion environment, we propose a scale-adaptive Kernel Correlation Filter (KCF) target tracking algorithm combined with the learning rate adjustment. Firstly, we use the KCF to obtain the initial position of the target, and then adopt a low-complexity scale estimation scheme to get the target's scale, which improves the ability of the proposed algorithm to adapt to the change of the target's scale, and the tracking speed is also ensured. Finally, we use the average difference between two adjacent images to analyze the change of the image, and adjust the learning rate of the target model in segments according to the average difference to solve the tracking failure problem when the target is severely obstructed. Compared the proposed algorithm with other five classic target tracking algorithms, the experimental results show that the proposed algorithm is well adapted to the complex environment such as target’s scale change, severe occlusion and background interference. At the same time, it has a real-time tracking speed of 231 frame/s.

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Adaptive scale object tracking with kernelized correlation filters

Cited by 13 publications

References 0 publications

Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms

Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms

Research on Target Loss Warning Method of KCF Algorithm Based on Outlier Detection

Scale Adaptive Kernel Correlation Filter Tracking Algorithm Combined with Learning Rate Adjustment

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