Target tracking algorithm for pedestrians movement based on kernel-correlation filtering

2020 ◽  
pp. 1-15
Author(s):  
Feng Du ◽  
Wan-Liang Wang ◽  
Zhi Zhang
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Kernel Correlation ◽  
Correlation Filtering

scholarly journals Target Tracking Algorithm Based on Adaptive Scale Detection Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dawei Yang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Target Tracking ◽  
Sudden Change ◽  
Tracking Algorithm ◽  
Learning Network ◽  
Template Update ◽  
Deep Learning Network ◽  
Kernel Correlation ◽  
Correlation Filtering

In this paper, to better solve the problem of low tracking accuracy caused by the sudden change of target scale, we design and propose an adaptive scale mutation tracking algorithm using a deep learning network to detect the target first and then track it using the kernel correlation filtering method and verify the effectiveness of the model through experiments. The improvement point of this paper is to change the traditional kernel correlation filtering algorithm to detect and track at the same time and to combine deep learning with traditional kernel correlation filtering tracking to apply in the process of target tracking; the addition of deep learning network not only can learn more accurate feature representation but also can more effectively cope with the low resolution of video sequences, so that the algorithm in the case of scale mutation achieves more accurate target tracking in the case of scale mutation. To verify the effectiveness of this method in the case of scale mutation, four evaluation criteria, namely, average accuracy, cross-ratio accuracy, temporal robustness, and spatial robustness, are combined to demonstrate the effectiveness of the algorithm in the case of scale mutation. The experimental results verify that the joint detection strategy plays a good role in correcting the tracking drift caused by the subsequent abrupt change of the target scale and the effectiveness of the adaptive template update strategy. By adaptively changing the number of interval frames of neural network redetection to improve the tracking performance, the tracking speed is improved after the fusion of correlation filtering and neural network, and the combination of both is promoted for better application in target tracking tasks.

Long Time Target Tracking Based on Kernel Correlation Filtering

2019 ◽  
Vol 56 (2) ◽  
pp. 021502
Author(s):  
杨剑锋 Yang Jianfeng ◽  
张建鹏 Zhang Jianpeng
Keyword(s):  
Target Tracking ◽  
Long Time ◽  
Kernel Correlation ◽  
Correlation Filtering

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

2018 ◽  
Vol 232 ◽  
pp. 03016 ◽  
Author(s):  
Di Wu ◽  
Li Peng
Keyword(s):  
Target Tracking ◽  
Initial Position ◽  
Learning Rate ◽  
Tracking Algorithm ◽  
Correlation Filter ◽  
Average Difference ◽  
Scale Change ◽  
Rate Adjustment ◽  
Adaptive Kernel ◽  
Kernel Correlation

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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