Unsupervised classification of erroneous video object trajectories

Sekh, Arif Ahmed; Dogra, Debi Prosad; Kar, Samarjit; Roy, Partha Pratim

doi:10.1007/s00500-017-2656-x

Cited by 5 publications

(4 citation statements)

References 59 publications

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“…e real-time nature of the video surveillance detection algorithm enables motion image segmentation of moving objects, rapid localization of the target human body, extraction of regions of interest, matching of location information, and also the identification of the behavioral dynamics of specific human bodies under complex conditions of multiple crowds, thus enabling object feature recognition [8]. Motion body segmentation technology is to segment the image sequence in the video according to specific criteria after reading the video file to form a certain region and then get the motion body region to be detected, as well as the human target features, shape, pose change state, and other pieces of information [9].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video

Liu

Wang

2021

Complexity

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We analyze and study the tracking of nonrigid complex targets of sports video based on mean shift fusion color histogram algorithm. A simple and controllable 3D template generation method based on monocular video sequences is constructed, which is used as a preprocessing stage of dynamic target 3D reconstruction algorithm to achieve the construction of templates for a variety of complex objects, such as human faces and human hands, broadening the use of the reconstruction method. This stage requires video sequences of rigid moving target objects or sets of target images taken from different angles as input. First, the standard rigid body method of Visuals is used to obtain the external camera parameters of the sequence frames as well as the sparse feature point reconstruction data, and the algorithm has high accuracy and robustness. Then, a dense depth map is computed for each input image frame by the Multi-View Stereo algorithm. The depth reconstruction with a too high resolution not only increases the processing time significantly but also generates more noise, so the resolution of the depth map is controlled by parameters. The multiple hypothesis target tracking algorithms are used to track multiple targets, while the chunking feature is used to solve the problem of mutual occlusion and adhesion between targets. After finishing the matching, the target and background models are updated online separately to ensure the validity of the target and background models. Our results of nonrigid complex target tracking by mean shift fusion color histogram algorithm for sports video improve the accuracy by about 8% compared to other studies. The proposed tracking method based on the mean shift algorithm and color histogram algorithm can not only estimate the position of the target effectively but also depict the shape of the target well, which solves the problem that the nonrigid targets in sports video have complicated shapes and are not easy to track. An example is given to demonstrate the effectiveness and adaptiveness of the applied method.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video

Liu

Wang

2021

Complexity

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“…The code runs in about 90 minutes for a 12 minutes bee-video in the hardware used. Previous approaches tracked the objects using only geometric rules just like Ahmed et al (AHMED et al, 2018) and ran in about 15 minutes for the same 12 minutes video. The disadvantage of the former method was the inability of learning: it always had the same assumptions for choosing the best next position for every bee.…”

Section: Discussionmentioning

confidence: 99%

“…The same simulation was used as input to an geometric algorithm, similar to the one described by Ahmed et al (AHMED et al, 2018): the possible bees in the frame n + 1 connect to the correspondent one in the frame n if they are inside a circumference with fixed radius (here, 30 pixels) and their segment forms the smallest angle from the ones inside the circumference. The main difference of the test is that the objects are assumed to be the same size, and the estimation of Eq.…”

Section: Comparison With the Unsupervised Geometric Algorithmmentioning

confidence: 99%

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Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information

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An Optimization Based deep LSTM Predictive Analysis for Decision Making in Cricket

Nelikanti

Reddy

Karuna³

2021

Innovative Data Communication Technologies and Application

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Unsupervised classification of erroneous video object trajectories

Cited by 5 publications

References 59 publications

[Retracted] Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video

[Retracted] Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video

Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information

An Optimization Based deep LSTM Predictive Analysis for Decision Making in Cricket

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