MotionSqueeze: Neural Motion Feature Learning for Video Understanding

Kwon, Heeseung; Kim, Manjin; Kwak, Suha; Cho, Minsu

doi:10.1007/978-3-030-58517-4_21

Cited by 118 publications

(90 citation statements)

References 42 publications

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“…In the formula, N represents the number of points on the outline. e characteristic description of the body behavior after normalization is normalized [24]. e normalization calculation formula is…”

Section: Body Behavior Characteristic Model Of Sports Trainingmentioning

confidence: 99%

Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

Zeng

2021

Security and Communication Networks

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In order to deal with the problems of low detection accuracy and efficiency existing in traditional detection methods of body behavior characteristics in sports training, this paper proposes a new detection method of body behavior characteristics in sports training based on the grey correlation model. According to this method, the foreground binary image is obtained by the interframe difference method, and the action information and contour information are extracted, firstly. And then, the behavior feature descriptor is obtained. After the description of body behavior characteristics in sports training is completed, the grey correlation coefficient is calculated, and the complete observation equation of time delay estimation of image grey value under complex sports training background is established to complete the detection of body behavior characteristics. The experimental results show that, compared with the traditional methods, the detection accuracy and efficiency of the text method can always maintain a high level, indicating that the practical application performance of this method is strong. In fact, the grey analysis does not attempt to find the best solution but does provide techniques for determining a good solution.

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Section: Body Behavior Characteristic Model Of Sports Trainingmentioning

confidence: 99%

Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

Zeng

2021

Security and Communication Networks

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“…Code is available at (accessed on 10 December 2021). Alternatively, the trainable neural module (MotionSqueeze) for effective motion feature extraction proposed in [ 82 ] could be exploited. Inserted in the middle of any neural network, it learns to establish correspondences across frames and convert them into motion features, which are readily fed to the next downstream layer for better prediction.…”

Section: Recent Advances In Human Motion Analysismentioning

confidence: 99%

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

Leo

Bernava

Carcagnì

et al. 2022

Sensors

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Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.

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“…Early approaches usually rely on hand-crafted features, which detect spatio-temporal interest points and then describe these points with local representations [45,46]. With the tremendous success of deep convolution networks on image-based classification tasks [12,35,38,41], researchers started to explore the application of deep networks on video action recognition task [7,18,29,30,54]. In [37], the famous twostream architecture is devised by applying two 2D CNN architectures separately on visual frames and staked opti-cal flows.…”

Section: Related Workmentioning

confidence: 99%

Representing Videos as Discriminative Sub-graphs for Action Recognition

Liu¹,

Qiu²,

Pan³

et al. 2022

Preprint

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Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.

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MotionSqueeze: Neural Motion Feature Learning for Video Understanding

Cited by 118 publications

References 42 publications

Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

Representing Videos as Discriminative Sub-graphs for Action Recognition

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