Mining Automatically Estimated Poses from Video Recordings of Top Athletes

Lienhart, Rainer; Einfalt, Moritz; Zecha, Dan

doi:10.2478/ijcss-2018-0005

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…We then use the remaining difference as the actual difference in shape. This is similar to the approach in [9] to capture body pose differences. In contrast to [4], we directly map the two mouth configurations we want to compare without an intermediate mapping onto a frontal template view of a mouth.…”

Section: A Basic Featuresmentioning

confidence: 88%

Detecting Speech Impairments from Temporal Visual Facial Features of Aphasia Patients

Einfalt

Lienhart

Lee

et al. 2019

2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

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We present an approach to detect speech impairments from video of people with aphasia, a neurological condition that affects the ability to comprehend and produce speech. To counter inherent privacy issues, we propose a crossmedia approach using only visual facial features to detect speech properties without listening to the audio content of speech. Our method uses facial landmark detections to measure facial motion over time. We show how to detect speech and pause instances based on temporal mouth shape analysis and identify repeating mouth patterns using a dynamic warping mechanism. We relate our developed features for pause frequency, mouth pattern repetitions, and pattern variety to actual symptoms of people with aphasia in the AphasiaBank dataset. Our evaluation shows that our developed features are able to reliably differentiate dysfluent speech production of people with aphasia from those without aphasia with an accuracy of 0.86. A combination of these handcrafted features and further statistical measures on talking and repetition improves classification performance to an accuracy of 0.88.

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Section: A Basic Featuresmentioning

confidence: 88%

Detecting Speech Impairments from Temporal Visual Facial Features of Aphasia Patients

Einfalt

Lienhart

Lee

et al. 2019

2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

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“…Specifically in the athletics domain, [5] use estimated athlete velocities from motion segmentation in videos for a high-level semantic classification of long jump actions. More recently, [21] use pose similarity and temporal structure in 2D pose sequences to segment athlete motion in long jump recordings into sequential phases.…”

Section: Related Workmentioning

confidence: 99%

Frame-Level Event Detection in Athletics Videos with Pose-Based Convolutional Sequence Networks

Einfalt

Dampeyrou

Zecha

et al. 2019

Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports

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In this paper we address the problem of automatic event detection in athlete motion for automated performance analysis in athletics. We specifically consider the detection of stride-, jump-and landing related events from monocular recordings in long and triple jump. Existing work on event detection in sports often uses manually designed features on body and pose configurations of the athlete to infer the occurrence of events. We present a two-step approach, where temporal 2D pose sequences extracted from the videos form the basis for learning an event detection model. We formulate the detection of discrete events as a sequence translation task and propose a convolutional sequence network that can accurately predict the timing of event occurrences. Our best performing architecture achieves a precision/recall of 92.3%/89.0% in detecting start and end of ground contact during the run-up and jump of an athlete at a temporal precision of ±1 frame at 200Hz. The results show that 2D pose sequences are a suitable motion representation for learning event detection in a sequence-to-sequence framework. CCS CONCEPTS• Computing methodologies → Visual content-based indexing and retrieval; Neural networks; Object detection. KEYWORDS event detection, video indexing, computer vision in sports, convolutional sequence modeling

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“…Behavior recognition can help users record their daily movement information and be used in some special scenarios, such as prisoner monitoring. Fall recognition can reduce the death of the elderly due to accidental slippage, sleep position detection can monitor sleep conditions and improve sleep quality, and exercise detection also plays a significant role in improving athletes' performance [ 2 , 3 ]. In addition, there are applications that use very mature step counting, sleep detection, and so on.…”

Section: Introductionmentioning

confidence: 99%

Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model

Wang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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Swimming monitoring based on acceleration sensor is an emerging research direction in the field of human motion recognition. As a public sport, swimming has a wide audience. The swimming monitoring system can facilitate people to monitor and record their own swimming data, so as to formulate a reasonable training plan. Aiming at the defects of single modal information representation ability, high contingency, and easy to be influenced by the outside world, this paper adopts the underwater posture training model of swimmers to perform multimodal information fusion. In this paper, a multimodal information fusion method based on evolutionary neural network is proposed, and an intelligent perception information processing model of the intelligent subject system is constructed. Aiming at the defect that the accuracy and speed of the underwater posture monitoring of swimmers cannot be guaranteed in a complex environment, an evolutionary neural network optimized by a multimodal adaptive genetic algorithm is constructed to perform multimodal information fusion to ensure the effectiveness of the system in the face of complex information. Regarding attitude detection, it mainly uses the three dimensions of the angle of movement, the influence of gravity, and the strength and speed of the movement to measure. The MPU6050 module processor has a wide range of applications and is a mold processing tool with high performance and level. It completes the data processing, data calculation, and data storage of the inspection system in this paper. This paper further studies the working principle, structure, and operation process of this module and adjusts the time error in the detection of carrier motion and attitude so that the processing function of this module can play an optimal state. Four kinds of swimming posture measurement experiments were carried out on the swimmers, and the experimental data were analyzed. The whole system is controlled by the host computer man-machine interaction software remotely and in real time through commands. The experimental results show that the system realizes the detection of the basic posture, meets the basic requirements of the system design, and provides a certain foundation for the follow-up research.

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Mining Automatically Estimated Poses from Video Recordings of Top Athletes

Cited by 9 publications

References 22 publications

Detecting Speech Impairments from Temporal Visual Facial Features of Aphasia Patients

Detecting Speech Impairments from Temporal Visual Facial Features of Aphasia Patients

Frame-Level Event Detection in Athletics Videos with Pose-Based Convolutional Sequence Networks

Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model

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