Content-Based Management of Human Motion Data: Survey and Challenges

Sedmidubský, Jan; Eliáš, Petr; Budikova, Petra; Zezula, Pavel

doi:10.1109/access.2021.3075766

Cited by 18 publications

(5 citation statements)

References 111 publications

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“…The k d-tree-based neighborhood search methods [ 17 , 18 , 21 , 23 , 25 ] require enough memory space to fit the k d-tree well into memory for proper running and execution of the system; otherwise, these methods do not scale well. Sedmidubsky et al [ 27 ] discuss the existing techniques of the content-based management of the skeleton data. They also discuss a few future research directions for the management of large and diverse motion capture skeleton data.…”

Section: Related Workmentioning

confidence: 99%

An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data

Yasin

Ghani

Krüger

2023

Sensors

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In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.

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Section: Related Workmentioning

confidence: 99%

An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data

Yasin

Ghani

Krüger

2023

Sensors

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“…The neural network structure calculates the kernel matrix based on the feature scores. Equation (3) is used to estimate the feature scores (Sedmidubsky et al, 2021 ).…”

Section: Technical Background and Design Of The Proposed Kelm-mff-bas...mentioning

confidence: 99%

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

Zhang

2022

Front. Neurorobot.

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The exploration here intends to compensate for the traditional human motion recognition (HMR) systems' poor performance on large-scale datasets and micromotions. To this end, improvement is designed for the HMR in sports competition based on the deep learning (DL) algorithm. First, the background and research status of HMR are introduced. Then, a new HMR algorithm is proposed based on kernel extreme learning machine (KELM) multidimensional feature fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results showed that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at 10 video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, support vector machine (SVM)-MFF-based HMR, convolutional neural network (CNN) + optical flow (CNN-T)-based HMR, improved dense trajectory (IDT)-based HMR, converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, by up to 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8 and 90.5% of the SVM-MFF-based HMR. Finally, the proposed KELM-MFF-based HMR algorithm takes 30 and 15 s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale datasets and capture and recognize micromotions. The research content provides a reference for applying extreme learning machine algorithms in sports competitions.

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“…A wide range of real-time MoCap systems and threedimensional (3D) human-like character animation systems are available for synthesizing realistic human motion data. Computer graphics professionals and researchers have used distinctive methods to construct numerous subject-specific motion databases to benefit the human-computer interaction researchers and the virtual reality user community [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Retroreflective or light-emitting diode markers-based optical sensors, video/marker-less tracking methods, and IMU-based wearable sensors are used to record human actions and store them as motion dataset. Currently, several human motions databases [1], [8], [15] are available that offer motion data in different formats (*.trc, *.htr, *.amc/asf, *.mvn, and *.c3d) [16], [17]. The 3D human motion data constitutes translatory and rotational motion related to the frontal, sagittal, and transverse planes.…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Sensed Human Motion Editing and Validation Through the Motion-Sphere

et al. 2022

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Synthesizing realistic human motion data using a real-time motion capture system in a controlled environment is a critical challenge. In addition, effectively manipulating the existing motion data is another primary concern and using such modified data in human motion analysis and activity recognition systems are prone to errors. This paper presents a simplistic and comprehensive system to effortlessly author, edit, and validate human motion data. The system enables a naive user to edit the existing motion data interactively using a humanoid model in a three-dimensional space, based on user-defined scenarios and synthesize numerous variations of the motion sequences. A modular concept of scenario-based sensed unit motion editing has been adopted to demonstrate the proposed system. We employed an efficient analytical kinematic and constraint solver to enforce the inherent body joint limits and external constraints while editing to synthesize complete and meaningful motion sequences. Furthermore, we substantiated the proposed sensed unit motion editing framework through a visual validation study using an open-source intuitive visualization tool called the Motion-Sphere. Finally, we compared the resultant synthesized motion against the real-time motion capture system data to verify the body segments' orientation and position accuracy deviations.

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Content-Based Management of Human Motion Data: Survey and Challenges

Cited by 18 publications

References 111 publications

An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data

An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

Scenario-Based Sensed Human Motion Editing and Validation Through the Motion-Sphere

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