检索结果-内蒙古大学图书馆

Fast Low-parameter video Activity Localization in Collaborative Learning Environments

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jatla, Venkatesh Teeparthi, Sravani Egala, Ugesh Pattichis, Sylvia Celedón Pattichis, Marios S. Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Curriculum and Instruction The University of Texas at Austin AustinTX78712-1293 United States

Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments. The majority of the research on video activity recognition is focused on the development of large parameter systems that require training on large video datasets. This paper develops a low-parameter, modular system with rapid inferencing capabilities that can be trained entirely on limited datasets without requiring transfer learning from large-parameter systems. The system can accurately detect and associate specific activities with the students who perform the activities in real-life classroom videos. Additionally, the paper develops an interactive web-based application to visualize human activity maps over long real-life classroom videos. Long-term video activity detection in real-life classroom videos present unique challenges, such as the need to detect multiple simultaneous activities, rapid transitions between activities, long-term occlusions, durations exceeding 15 minutes, and numerous individuals performing similar activities in the background. Moreover, subtle hand movements further complicate the need to differentiate between actual typing and writing activities as opposed to unrelated hand movements. The system processes the input videos using fast activity initializations and current methods for object detection to determine the location and the the person performing the activities. These regions are then processed through an optimal low-parameter dyadic 3D-CNN classifier to identify the activity. The proposed system processes 1 hour of video in 15 minutes for typing and 50 minutes for writing activities. The system uses several methods to optimize the inference pipeline. For each activity, the system determines an optimal low-parameter 3D CNN architecture selected from a family of low-parameter architectures. The input video is broken into smaller video regions that are transcoded at an optimized frame rate. For inference, a

关键词： Object detection

Long-term Human Participation Assessment In Collaborative Learning Environments Using Dynamic Scene Analysis

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Shi, Wenjing Tran, Phuong Celedón-Pattichis, Sylvia Pattichis, Marios S. Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States Department of Curriculum and Instruction The University of Texas at Austin AustinTX78712-1293 United States

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state-of-the-art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos. © 2024, CC BY.

关键词： Students

Talking Detection in Collaborative Learning Environments 19th

学校读者我要写书评

暂无评论

Talking Detection in Collaborative Learning Environments

19th International Conference on Computer Analysis of images and Patterns, CAIP 2021

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Departtment of Language Literacy and Sociocultural Studies University of New Mexico AlbuquerqueNM United States

ISBN: (纸本)9783030891305

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. © 2021, Springer Nature Switzerland AG.

关键词： Computer aided instruction

Person Detection in Collaborative Group Learning Environments Using Multiple Representations

学校读者我要写书评

暂无评论

Person Detection in Collaborative Group Learning Environment...

Asilomar Conference on Signals, Systems & Computers

作者： Wenjing Shi Marios S. Pattichis Sylvia Celedón-Pattichis Carlos LópezLeiva Image and Video Processing and Communications Lab (ivpcl.unm.edu) University of New Mexico United States University of New Mexico United States

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours).We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone.

关键词： Computers Frequency modulation Face recognition Collaboration image representation Face detection videos

Person Detection in Collaborative Group Learning Environments Using Multiple Representations

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos image and video Processing and Communications Lab [ivpcl.unm.edu Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours). We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone. © 2021, CC BY.

关键词： Students

Talking detection in collaborative learning environments

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

关键词： Speech recognition

Discrete, recurrent, and scalable patterns in human judgement underlie affective picture ratings

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Azcona, Emanuel A. Kim, Byoung-Woo Vike, Nicole L. Bari, Sumra Lalvani, Shamal Stefanopoulos, Leandros Woodward, Sean Block, Martin Katsaggelos, Aggelos K. Breiter, Hans C. Image and Video Processing Lab Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States Department of Psychiatry and Behavioral Sciences Northwestern University ChicagoIL United States Integrated Marketing Communications Northwestern University EvanstonIL United States Department of Computer Science Northwestern University EvanstonIL United States Department of Radiology Northwestern University ChicagoIL United States Laboratory of Neuroimaging and Genetics Department of Psychiatry Massachusetts General Hospital Harvard School of Medicine BostonMA United States

Operant keypress tasks, where each action has a consequence, have been analogized to the construct of "wanting" and produce lawful relationships in humans that quantify preferences for approach and avoidance behavior. It is unknown if rating tasks without an operant framework, which can be analogized to "liking", show similar lawful relationships. We studied three independent cohorts of participants (N = 501, 506, and 4,019 participants) collected by two distinct organizations, using the same 7-point Likert scale to rate negative to positive preferences for pictures from the International Affective Picture Set. Picture ratings without an operant framework produced similar value functions, limit functions, and tradeoff functions to those reported in the literature for operant keypress tasks, all with Rn goodness of fits above 0.75. These value, limit, and trade-off functions were discrete in their mathematical formulation, recurrent across all three independent cohorts, and demonstrated scaling between individual and group curves. In all three experiments, the computation of loss aversion showed 95% confidence intervals below the value of 2, arguing against a strong overweighting of losses relative to gains, as has previously been reported for keypress tasks or games of chance with calibrated uncertainty. Graphed features from the three cohorts were similar and argue that preference assessments meet three of four criteria for lawfulness, providing a simple, short, and low-cost method for the quantitative assessment of preference without forced choice decisions, games of chance, or operant keypressing. This approach can easily be implemented on any digital device with a screen (e.g., cellphones). © 2022, CC BY.

关键词： Big data

Robust Head Detection in Collaborative Learning Environments Using AM-FM Representations

学校读者我要写书评

暂无评论

Robust Head Detection in Collaborative Learning Environments...

IEEE Southwest Symposium on image Analysis and Interpretation

作者： Wenjing Shi Marios S. Pattichis Sylvia Celedón-Pattichis Carlos LópezLeiva image and video Processing and Communications Lab (ivpcl.unm.edu) University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

The paper introduces the problem of robust head detection in collaborative learning environments. In such environments, the camera remains fixed while the students are allowed to sit at different parts of a table. Example challenges include the fact that students may be facing away from the camera or exposing different parts of their face to the camera. To address these issues, the paper proposes the development of two new methods based on Amplitude Modulation-Frequency Modulation (AM-FM) models. First, a combined approach based on color and FM texture is developed for robust face detection. Secondly, a combined approach based on processing the AM and FM components is developed for robust, back of the head detection. The results of the two approaches are also combined to detect all of the students sitting at each table. The robust face detector achieved 79% accuracy on a set of 1000 face image examples. The back of the head detector achieved 91% accuracy on a set of 363 test image examples.

关键词： Hair Frequency modulation Face Detectors Robustness image color analysis

Context-Sensitive Human Activity Classification in Collaborative Learning Environments

学校读者我要写书评

暂无评论

Context-Sensitive Human Activity Classification in Collabora...

IEEE Southwest Symposium on image Analysis and Interpretation

作者： Abigail Ruth Jacoby Marios S. Pattichis Sylvia Celedón-Pattichis Carlos LópezLeiva image and video Processing and Communications Lab (ivpcl.unm.edu) University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

Human activity classification remains challenging due to the strong need to eliminate structural noise, the multitude of possible activities, and the strong variations in video acquisition. The current paper explores the study of human activity classification in a collaborative learning *** paper explores the use of color based object detection in conjunction with contextualization of object interaction to isolate motion vectors specific to each human activity. The basic approach is to make use of separate classifiers for each activity. Here, we consider the detection of typing, writing, and talking activities in raw *** method was tested using 43 uncropped video clips with 620 video frames for writing, 1050 for typing, and 1755 frames for talking. Using simple KNN classifiers, the method gave accuracies of 72.6% for writing, 71% for typing and 84.6% for talking. Classification accuracy improved to 92.5% (writing), 82.5% (typing) and 99.7% (talking) with the use of Deep Neural Networks.

关键词： Writing Keyboards Feature extraction image color analysis Neural networks Collaborative work Monitoring