Dynamic Group Interactions in Collaborative Learning Videos

Shi, Wenjing; Pattichis, Marios S.; Celedón‐Pattichis, Sylvia; LópezLeiva, Carlos

doi:10.1109/acssc.2018.8645132

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…Eiliar et al [15] provides a maintainable open-source activity system. Detection of attention traits is investigated by Shi et al [16], using AM-FM models to detect head direction and group interactions.…”

Section: Related Researchmentioning

confidence: 99%

Speaker Diarization and Identification From Single Channel Classroom Audio Recordings Using Virtual Microphones

2022

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Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphone per student group without any access to previous large datasets for training. This dissertation proposes a method of speaker identification using crosscorrelation patterns associated to an array of virtual microphones, centered around the physical microphone. The virtual microphones are simulated by using approximate speaker geometry observed from a video recording. The patterns are constructed based on estimates

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Section: Related Researchmentioning

confidence: 99%

Speaker Diarization and Identification From Single Channel Classroom Audio Recordings Using Virtual Microphones

2022

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“…Then, further processing would determine where students are focused their attention. In [6] the same authors used AM-FM methods to determine student group interactions such as human to human, human to others and no interactions.…”

Section: Previous Am-fm Researchmentioning

confidence: 99%

“…Additional references for the AM-FM theory were used from [13]. Also, regarding the Gabor filterbank with DCA, AM-FM methods has been used as a feature extraction for head and hair detection in group interactions, refer to [10] and [11].…”

Section: Am-fm Demodulationmentioning

confidence: 99%

The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks

Sanchez

Pattichis

Celedón‐Pattichis

et al. 2020

2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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Large scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed.This work examines the impact of using AM-FM representations as input images for CNN classification applications. A comparison was made between AM-FM components combinations and gray scale images as inputs for reduced and complete networks.The results showed that only the phase component produced significant predictions within a simple network. Neither IA or gray scale image were able to induce any learning in the system. Furthermore, the FM results were 7x faster during training and used 123x less parameters compared to state-of-the-art MobileNetV2 architecture, while maintaining comparable performance (AUC of 0.78 vs 0.79).

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“…The current paper dramatically extends this prior research that was focused on very short video datasets without considering occlusion, appearance issues, and associating hands with different people. We also note that head detection and person recognition has been studied in [14], [15], [19] and [17]. Human activity classification over cropped regions was studied in [5], [16] and [8].…”

Section: Introductionmentioning

confidence: 99%

Fast Hand Detection in Collaborative Learning Environments

Teeparthi¹,

Jatla²,

Pattichis³

et al. 2021

Preprint

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Long-term object detection requires the integration of framebased results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related example, we study hand detection over long video recordings in collaborative learning environments. More specifically, we develop long-term hand detection methods that can deal with partial occlusions and dramatic changes in appearance. Our approach integrates object-detection, followed by time projections, clustering, and small region removal to provide effective hand detection over long videos. The hand detector achieved average precision (AP) of 72% at 0.5 intersection over union (IoU). The detection results were improved to 81% by using our optimized approach for data augmentation. The method runs at 4.7×the real-time with AP of 81% at 0.5 intersection over the union. Our method reduced the number of false-positive hand detections by 80% by improving IoU ratios from 0.2 to 0.5. The overall hand detection system runs at 4× real-time.

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Dynamic Group Interactions in Collaborative Learning Videos

Cited by 11 publications

References 13 publications

Speaker Diarization and Identification From Single Channel Classroom Audio Recordings Using Virtual Microphones

Speaker Diarization and Identification From Single Channel Classroom Audio Recordings Using Virtual Microphones

The Importance of the Instantaneous Phase for Face Detection using Simple Convolutional Neural Networks

Fast Hand Detection in Collaborative Learning Environments

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