SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior

• URL: http://arxiv.org/abs/2310.02522v2
• Date: Mon, 9 Sep 2024 11:01:25 GMT
• Title: SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior
• Authors: Fan Yang, Tao Wang,
• Abstract summary: Student Classroom Behavior dataset (SCB-dataset3) represents real-life scenarios. Our dataset comprises 5686 images with 45578 labels, focusing on six behaviors: hand-raising, reading, writing, using a phone, bowing the head, and leaning over the table.
• Score: 6.878489963907169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of deep learning methods to automatically detect students' classroom behavior is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available datasets on student behavior poses a challenge for researchers in this field. To address this issue, we propose the Student Classroom Behavior dataset (SCB-dataset3), which represents real-life scenarios. Our dataset comprises 5686 images with 45578 labels, focusing on six behaviors: hand-raising, reading, writing, using a phone, bowing the head, and leaning over the table. We evaluated the dataset using the YOLOv5, YOLOv7, and YOLOv8 algorithms, achieving a mean average precision (map) of up to 80.3$\%$. We believe that our dataset can serve as a robust foundation for future research in student behavior detection and contribute to advancements in this field. Our SCB-dataset3 is available for download at: https://github.com/Whiffe/SCB-dataset

Related papers

• Student Classroom Behavior Detection based on Spatio-Temporal Network and Multi-Model Fusion [6.069671582146248]
  Deep learning methods to detect students' classroom behavior automatically is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available datasets on student behavior, as well as the high cost of manually labeling such datasets, pose significant challenges for researchers in this field. We proposed a method for extending the Student Classroom Scenarios dataset through image-temporal behavior datasets.
  arXiv  Detail & Related papers  (2023-10-25T00:46:26Z)
• DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection [72.25697820290502]
  This work introduces a straightforward and efficient strategy to identify potential novel classes through zero-shot classification. We refer to this approach as the self-training strategy, which enhances recall and accuracy for novel classes without requiring extra annotations, datasets, and re-training. Empirical evaluations on three datasets, including LVIS, V3Det, and COCO, demonstrate significant improvements over the baseline performance.
  arXiv  Detail & Related papers  (2023-10-02T17:52:24Z)
• Student Classroom Behavior Detection based on Improved YOLOv7 [3.6119958671506707]
  We propose the Student Classroom Behavior Detection method, based on improved YOLOv7. First, we created the Student Classroom Behavior dataset (SCB-Dataset), which includes 18.4k labels and 4.2k images. To improve detection accuracy in crowded scenes, we integrated the biformer attention module and Wise-IoU into the YOLOv7 network. Experiments were conducted on the SCB-Dataset, and the model achieved an mAP@0.5 of 79%, resulting in a 1.8% improvement over previous results.
  arXiv  Detail & Related papers  (2023-06-06T00:01:40Z)
• Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model Fusion [8.800332201027299]
  We propose the Student Classroom Behavior Detection system based on based on YOLOv7-BRA. We identified eight different behavior patterns, including standing, sitting, speaking, listening, walking, raising hands, reading, and writing. We conducted experiments on the SCB-Dataset, and YOLOv7-BRA achieved an mAP@0.5 of 87.1%, resulting in a 2.2% improvement over previous results.
  arXiv  Detail & Related papers  (2023-05-13T02:46:41Z)
• SCB-dataset: A Dataset for Detecting Student Classroom Behavior [3.6119958671506707]
  This dataset comprises 7428 images and 106830 labels across 20 classes. We believe that SCB-Dataset5 can provide a solid foundation for future applications of artificial intelligence in education.
  arXiv  Detail & Related papers  (2023-04-05T15:02:30Z)
• Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection [90.32180043449263]
  State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. This paper introduces a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework.
  arXiv  Detail & Related papers  (2023-04-04T02:09:32Z)
• Is margin all you need? An extensive empirical study of active learning on tabular data [66.18464006872345]
  We analyze the performance of a variety of active learning algorithms on 69 real-world datasets from the OpenML-CC18 benchmark. Surprisingly, we find that the classical margin sampling technique matches or outperforms all others, including current state-of-art.
  arXiv  Detail & Related papers  (2022-10-07T21:18:24Z)
• Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning [16.457778420360537]
  We propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We develop a model that learns strong representations from accelerometer signals, while reducing the model's reliance on class labels. We evaluate the performance of the proposed solution on three datasets, namely MotionSense, HAPT, and HHAR.
  arXiv  Detail & Related papers  (2022-08-26T22:47:18Z)
• Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
  It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
  arXiv  Detail & Related papers  (2022-05-02T16:09:17Z)
• SLADE: A Self-Training Framework For Distance Metric Learning [75.54078592084217]
  We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings.
  arXiv  Detail & Related papers  (2020-11-20T08:26:10Z)
• Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
  Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
  arXiv  Detail & Related papers  (2020-01-06T13:37:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.