Motion pattern based representation for improving human action retrieval

Ramezani, Mohsen

doi:10.1007/s11042-018-5835-6

Cited by 9 publications

(4 citation statements)

References 73 publications

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“…On the one hand, feature extraction can reduce the dimensionality of the original human motion data, avoiding the direct similarity comparison of high-dimensional human motion data during retrieval, and, on the other hand, effective features can represent the semantics of human motion [24,25]. e matching of features is more in line with people's cognition of sports, and the retrieval results are more accurate.…”

Section: Motion Feature Definition and Feature Extractionmentioning

confidence: 99%

Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

Bao

Yao

2020

Complexity

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In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data based on motion capture in index space. By extracting key frames from the original motion to perform horizontal dimensionality reduction and defining features based on Laban motion analysis, the motion segment is subjected to vertical feature dimensionality reduction. After extracting features from the input motion segment, motion matching is performed on the index space. This paper designs the optimization method of the phased dynamic time deformation algorithm in time efficiency and analyzes the optimization method of the phased dynamic time deformation algorithm in time complexity. Considering the time efficiency redundancy, this paper optimizes the time complexity of the phased dynamic time deformation method. This improves the time efficiency of the staged dynamic time warping algorithm, making it suitable for larger-scale human motion data problems. Experiments show that the method in this paper has the advantage of speed, is more in line with the semantics of human motion, and can meet the retrieval requirements of human motion databases.

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Section: Motion Feature Definition and Feature Extractionmentioning

confidence: 99%

Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

Bao

Yao

2020

Complexity

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“…[25,10] suggest hand drawn sketch based skeleton sequence retrieval methods. On the other end of the spectrum, sequence based motion features utilize global properties of the motion sequence [4,14,8]. Muller et al [14] presented the motion template (MT) in which motions of the same class can be represented by an explicit interpretable matrix using a set of boolean geometric feature.…”

Section: Related Workmentioning

confidence: 99%

“…3D Human Motion Retrieval. Most of the existing retrieval methods [27,8,28] show results on only upto 10 classes, and on very small datasets. [15] use the same number of class labels as us, and we therefore compare with them in Fig- ure 4a.…”

Section: Comparison With State Of the Artmentioning

confidence: 99%

DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences

Battan

Venkat

Sharma

2020

Lecture Notes in Computer Science

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3D Human Motion Indexing and Retrieval is an interesting problem due to the rise of several data-driven applications aimed at analyzing and/or re-utilizing 3D human 12 skeletal data, such as datadriven animation, analysis of sports bio-mechanics, human surveillance etc. Spatio-temporal articulations of humans, noisy/missing data, different speeds of the same motion etc. make it challenging and several of the existing state of the art methods use hand-craft features along with optimization based or histogram based comparison in order to perform retrieval. Further, they demonstrate it only for very small datasets and few classes. We make a case for using a learned representation that should recognize the motion as well as enforce a discriminative ranking. To that end, we propose, a 3D human motion descriptor learned using a deep network. Our learned embedding is generalizable and applicable to real-world data -addressing the aforementioned challenges and further enables sub-motion searching in its embedding space using another network. Our model exploits the inter-class similarity using trajectory cues, and performs far superior in a self-supervised setting. State of the art results on all these fronts is shown on two large scale 3D human motion datasets -NTU RGB+D and HDM05.

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“…Ren et al [14] proposed a video-based human data retrieval approach, and convolutional neural network is applied for extracting motion feature. Furthermore, in order to evaluate accurate information of the actions and effectiveness of models, the complexity of pattern of body motions on different scales was calculated to describe human action [15]. In addition, Tang et al [16] presented integrated framework of human motion retrieval by sketching several key poses.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

2021

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Human motion retrieval and analysis is a useful means of activity recognition to 3D human bodies. An efficient method is proposed to estimate human motion by using symmetric joint points and limb features of various limb parts based on regression task. We primarily obtain the 3D coordinates of symmetric joint points based on the located waist and hip points. By introducing three critical feature points on torso and symmetric joint points’ matching on motion video sequences, the 3D coordinates of symmetric joint points and its asymmetric limb features will not be affected by shading and interference of limb on different postures. With the asymmetric limb features of various human parts, a dynamic regulated Fuzzy neural network (DRFNN) is proposed to estimate human motion for different asymmetric postures using learning algorithm of network parameters and weights. Finally, human sequential actions corresponding to different asymmetric postures are presented according to the best retrieval results by DRFNN based on 3D human action database. Experiments show that compared with the traditional adaptive self-organizing fuzzy neural network (SOFNN) model, the proposed algorithm has higher estimation accuracy and better presentation results compared with the existing human motion analysis algorithms.

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Motion pattern based representation for improving human action retrieval

Cited by 9 publications

References 73 publications

Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences

An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

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