Kalman Filter Algorithm for Sports Video Moving Target Tracking

Gui, Quanan; Xia, Yunjian

doi:10.1109/icaaci50733.2020.00010

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…We can also use the association effect by proximity to reinforce the tracking by Kalman. In addition to the domains mentioned above, uses of Kalman filters in tracking are possible to allow, for example, in the field of sports to identify a player at any time [16] or in the field of security to fill the gaps in the movement of a person who would pass behind an obstacle for example [24]. However, due to the computational power required to perform both detection and tracking, there is very little work on the subject in the field of urban tracking.…”

Section: Kalman Filter Trackingmentioning

confidence: 99%

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Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

Auguste

Kaddah

Elbouz

et al. 2021

Sensors

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In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

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Section: Kalman Filter Trackingmentioning

confidence: 99%

“…This technique is for example used in some clustering algorithms such as DBSCAN [15]. In a second step, another method based on Kalman filters [16,17] would allow using the known data in N-1, but also would keep a trace of the data in N-X to predict how the point cloud could evolve in N.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

Auguste

Kaddah

Elbouz

et al. 2021

Sensors

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“…Generative tracking methods usually represent the target by a model and search for the interested area to find the position that is most similar to the model. Some well-known generation methods are the Kalman filter, 6 particle filter, 7 , 8 mean-shift, 9 – 11 and so on. Discriminative tracking methods divide the target and background into positive and negative samples and try to separate each; they include tracking-learning-detection (TLD) 12 and Struck 13 .…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking method based on Bayesian classifier for pulse array image sensor

Zhang

Gao

et al. 2023

Opt. Eng.

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.A pulse array image sensor (PAIS) is a bionic image sensor that converts light intensity into a pulse sequence to reduce the data volume. The traditional tracking method is not suitable for the sparse data form because of the absence of grayscale information. A trajectory tracking method based on a Bayesian classifier is proposed to maximize the property of the pulse data. First, candidate points with the smallest interval distances are selected in the area of interest. Then the total pulse numbers in a specified period at different positions are gathered to compose the positive and negative feature vectors, which are used to train a naive Bayesian classifier. The classifier can exactly obtain positions from the candidate points, and features in the new tracking positions train and update the classifier. The two-step filtering target point tracking algorithm using the interval distance and Bayesian classifier requires only the raw pulse data without processing, which can maximize the advantages of the special data format and improve computing efficiency. In this way, the position information can be directly obtained from pulse data, and the problem of wasting computing resources on reconstructing grayscale images and treating them in the traditional way is avoided. Experiments were performed on both real filmed data and the public event camera datasets. Our method can obtain trajectories with a high accuracy and long tracking time. The tracking errors are in single digits. In the comparison experiments, although our method has a smaller data volume than other models that obtain both frame and event data, the results still show that it has a comparable performance to the state-of-the-art methods.

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“…However, severe occlusion and large areas of similar background can still lead to tracking losses. To address this issue, Gui et al [10] introduced an improved tracking algorithm based on adaptive Kalman filtering that reduces interference. It adjusts the weights of different features using an adaptive approach, which allows for increased tracking accuracy and stability in scenarios where there are variations in the target and background.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5

et al. 2023

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In recent years, human interference in seismic-station environments has posed challenges to the quality and accuracy of seismic signals, making data processing difficult. To accurately identify interference caused by personnel and ensure the reliability of seismic-network instrument detection data, it is necessary to track the detected targets across consecutive frames. Deep neural networks have made significant progress in this field. Therefore, an intelligent identification solution for environmental interference at seismic stations is proposed, which combines deep learning with multi-object tracking techniques. A centroid-matching tracking algorithm based on Kalman filtering is introduced to identify the entry/exit timestamps, alongside motion trajectories of interfering individuals, thereby marking the anomalous data caused by the presence of interfering personnel in seismic time-series data. Experimental results demonstrate that this research provides an effective solution for intelligent identification of environmental interference in seismic station environments.

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Kalman Filter Algorithm for Sports Video Moving Target Tracking

Cited by 8 publications

References 6 publications

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

Trajectory tracking method based on Bayesian classifier for pulse array image sensor

Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5

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