Relevant bibliographies by topics / Single target tracking algorithms

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  1. Journal articles

Academic literature on the topic 'Single target tracking algorithms'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Single target tracking algorithms"

1

Ling, Jiankun. "Target Tracking Using Kalman Filter Based Algorithms." Journal of Physics: Conference Series 2078, no. 1 (2021): 012020. http://dx.doi.org/10.1088/1742-6596/2078/1/012020.

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Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
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2

ZhongMing Liao and Azlan Ismail. "Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms." Journal of Smart Science and Technology 3, no. 1 (2023): 63–79. http://dx.doi.org/10.24191/jsst.v3i1.42.

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Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. Experiments were conducted on OTB100 and VOT2018 benchmark datasets, and the experimental data obtained were analysed to derive two visual single-target tracking algorithms with optimal tracking performance. Finally, the future development of tracking algorithms is envisioned.
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3

Qu, Zhiyi, Xue Zhao, Huihui Xu, Hongying Tang, Jiang Wang, and Baoqing Li. "An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking." Sensors 22, no. 18 (2022): 6972. http://dx.doi.org/10.3390/s22186972.

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Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.
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4

Zhang, Haozheng, Xiong Li, and Yu Meng. "Performance Study of Two Bearings-only Target Tracking Algorithms." Journal of Physics: Conference Series 2419, no. 1 (2023): 012086. http://dx.doi.org/10.1088/1742-6596/2419/1/012086.

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Abstract For the bearings-only target tracking for single array, the tracking performance of extended kalman filter algorithm in cartesian coordinates and modified polar coordinates is studied. The result shows that the performance of extended kalman filter algorithm in polar coordinates is more general than that in cartesian coordinates. In addition, the tracking performance of these two algorithms decreases with an increase in azimuth measurement error.
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5

Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.

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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.
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6

Wei, Hao, Zong-ping Cai, Bin Tang, and Ze-xiang Yu. "Review of the algorithms for radar single target tracking." IOP Conference Series: Earth and Environmental Science 69 (June 2017): 012073. http://dx.doi.org/10.1088/1755-1315/69/1/012073.

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7

Zhang, Ming, Li Wang, Hai Hua Shi, and Wei Xiang. "The Target Tracking Algorithm Research of Independent Vision Robot Fish." Advanced Materials Research 753-755 (August 2013): 2015–19. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2015.

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In the independent vision robot fish games, the interference of water wave often causes tracking inaccuracy and target tracking failure. In order to solve these problems, the Meanshift algorithm and the combination of Meanshift algorithm and Kalman filter respectively are studied to realize target tracking of independent vision robot fish in this paper. By comparing the two algorithms, the results show that: the former tracking algorithm is not ideal and easy to lose the target. The combined algorithm of Meanshift and Kalman filter can effectively improve the performance of single-target tracking in a complex environment to achieve the goal of continuous accurate tracking.
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8

Chang-Jian Wang, Chang-Jian Wang, Yong Ding Chang-Jian Wang, and Ye Ji Yong Ding. "An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD." 電腦學刊 33, no. 2 (2022): 069–81. http://dx.doi.org/10.53106/199115992022043302006.

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<p>This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale search strategy is proposed. In this strategy, the scale value is searched in a tree shape according to the response size, which reduces the number of scale searches. Finally, the average peak correlation energy is used for occlusion determination, and the model update strategy is improved, thereby enhancing the algorithm’s ability to track occluded targets. The results of experimental evaluation and comparison on the OTB100 and UAV123 data sets show that when the target has complex conditions such as scale changes, similar interference, occlusion, etc., the proposed algorithm performs well in mainstream related filtering algorithms. Through the quantitative and qualitative analysis of the experimental results, the effectiveness of the proposed algorithm is verified.</p> <p> </p>
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9

Chang-Jian Wang, Chang-Jian Wang, Yong Ding Chang-Jian Wang, and Ye Ji Yong Ding. "An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD." 電腦學刊 33, no. 2 (2022): 069–81. http://dx.doi.org/10.53106/199115992022043302006.

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<p>This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale search strategy is proposed. In this strategy, the scale value is searched in a tree shape according to the response size, which reduces the number of scale searches. Finally, the average peak correlation energy is used for occlusion determination, and the model update strategy is improved, thereby enhancing the algorithm’s ability to track occluded targets. The results of experimental evaluation and comparison on the OTB100 and UAV123 data sets show that when the target has complex conditions such as scale changes, similar interference, occlusion, etc., the proposed algorithm performs well in mainstream related filtering algorithms. Through the quantitative and qualitative analysis of the experimental results, the effectiveness of the proposed algorithm is verified.</p> <p> </p>
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10

Guo, Xifeng, Turdi Tohti, Mayire Ibrayim, and Askar Hamdulla. "Research on Anti-Occlusion Correlation Filtering Tracking Algorithm Based on Adaptive Scale." Information 13, no. 3 (2022): 131. http://dx.doi.org/10.3390/info13030131.

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Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new developments have been made in this research. However, traditional correlation filtering algorithms cannot achieve real-time tracking in complex scenes such as illumination changes, target occlusion, motion deformation, and motion blur due to their single characteristics and insufficient background information. Therefore, a scale-adaptive anti-occlusion correlation filtering tracking algorithm is proposed. First, solve the single feature problem of traditional correlation filters through feature fusion. Secondly, the scale pyramid is introduced to solve the problem of tracking failure caused by scale changes. In this paper, two independent filters are trained, namely the position filter and the scale filter, to locate and scale the target, respectively. Finally, an occlusion judgment strategy is proposed to improve the robustness of the algorithm in view of the tracking drift problem caused by the occlusion of the target. In addition, the problem of insufficient background information in traditional correlation filtering algorithms is improved by adding context-aware background information. The experimental results show that the improved algorithm has a significant improvement in success rate and accuracy compared when with the traditional kernel correlation filter tracking algorithm. When the target has large scale changes or there is occlusion, the improved algorithm can still keep stable tracking.
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