HMR-vid: a comparative analytical survey on human motion recognition in video data

Keyvanpour, Mohammad Reza; Vahidian, Shokofeh; Ramezani, Mahin

doi:10.1007/s11042-020-09485-2

Cited by 22 publications

(14 citation statements)

References 203 publications

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“…In this paper, we first use the extended Kalman filter to predict the center of the target and determine the predicted position of each chunk based on the relative position of the chunk center and the target center; then, we set a certain scale at the chunk center and match the feature points in its range with the feature points in the original model center chunk and define the new center by the position of the successfully matched feature points [22]. e new central chunk position is defined by the position of the successfully matched feature points so that the new chunk center position can be obtained and the model can be modified.…”

Section: Experimental Design Of Nonrigid Complex Target Trackingmentioning

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: Experimental Design Of Nonrigid Complex Target Trackingmentioning

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 SVM has shown its ability in an unbalanced dataset because it only considers the support vector. This classifier is computationally efficient and can achieve good performance at high differences between classes and low differences [ 42 ].…”

Section: Challenge Of Harmentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Serpush

Menhaj

Masoumi

et al. 2022

Computational Intelligence and Neuroscience

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Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

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“…For high-level feature representation, the literature [9] uses an ordering function to model the evolution of motion over time. To better capture spatiotemporal information, literature [10] uses hidden Markov models to capture temporal information in videos and uses fixed dimensional vectors as descriptors of motion videos. The literature [11] uses a structural trajectory learning approach to extract relevant motion features.…”

Section: Related Workmentioning

confidence: 99%

Optimization of a Wireless Sensor-Based Tennis Motion Pattern Recognition System

Ruan

Zhang

2022

Journal of Sensors

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With the rapid development of information technology in today’s era, the application of the Internet, big data, and smart bracelet information technology in the field of sports has enhanced the intelligence of sports and plays an important role in promoting sports performance. This paper focuses on the application of wireless sensors in the field of tennis, using research methods such as literature research, video analysis, comparative research, and mathematical statistics, to explore and analyze the application of wireless sensors in the field of tennis big data, tennis robotics, and the implementation of tennis teaching and training, to provide a theoretical basis for promoting the application of wireless sensors in the field of tennis and also for the broader application of wireless sensors in sports to provide a theoretical reference. For the problem of multiple scales of motion targets in action videos, two video action recognition methods based on high- and low-level feature fusion are proposed, which are the video action recognition methods based on top-down feature fusion and the video action recognition methods based on bottom-up feature fusion. The multipowered mobile anchor nodes are allowed to move along a prescribed route and broadcast multiple power signals, and then, the location of the unknown node is estimated using a four-ball intersection weight center-of-mass algorithm. Simulations show experimentally that the algorithm reduces the average localization error and requires fewer anchor nodes.

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HMR-vid: a comparative analytical survey on human motion recognition in video data

Cited by 22 publications

References 203 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

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Optimization of a Wireless Sensor-Based Tennis Motion Pattern Recognition System

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