Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model Fusion

• URL: http://arxiv.org/abs/2305.07825v2
• Date: Mon, 9 Sep 2024 11:08:17 GMT
• Title: Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model Fusion
• Authors: Fan Yang, Tao Wang, Xiaofei Wang,
• Abstract summary: 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.
• Score: 8.800332201027299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately detecting student behavior in classroom videos can aid in analyzing their classroom performance and improving teaching effectiveness. However, the current accuracy rate in behavior detection is low. To address this challenge, we propose the Student Classroom Behavior Detection system based on based on YOLOv7-BRA (YOLOv7 with Bi-level Routing Attention ). We identified eight different behavior patterns, including standing, sitting, speaking, listening, walking, raising hands, reading, and writing. We constructed a dataset, which contained 11,248 labels and 4,001 images, with an emphasis on the common behavior of raising hands in a classroom setting (Student Classroom Behavior dataset, SCB-Dataset). To improve detection accuracy, we added the biformer attention module to the YOLOv7 network. Finally, we fused the results from YOLOv7 CrowdHuman, SlowFast, and DeepSort models to obtain student classroom behavior data. 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. Our SCB-dataset can be downloaded from: https://github.com/Whiffe/SCB-datase

Related papers

• BAL: Balancing Diversity and Novelty for Active Learning [53.289700543331925]
  We introduce a novel framework, Balancing Active Learning (BAL), which constructs adaptive sub-pools to balance diverse and uncertain data. Our approach outperforms all established active learning methods on widely recognized benchmarks by 1.20%.
  arXiv  Detail & Related papers  (2023-12-26T08:14:46Z)
• 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)
• A Spatio-Temporal Attention-Based Method for Detecting Student Classroom Behaviors [3.6119958671506707]
  Low accuracy in student classroom behavior detection is a prevalent issue. We propose a Spatio-Temporal Attention-Based Method for Detecting Student Classroom Behaviors (BDSTA) Compared with the SlowFast model, the average accuracy of student behavior classification detection improves by 8.94% using BDSTA.
  arXiv  Detail & Related papers  (2023-10-04T01:47:36Z)
• SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior [6.878489963907169]
  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.
  arXiv  Detail & Related papers  (2023-10-04T01:43:46Z)
• 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)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• 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)
• To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection [95.21700830273221]
  Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. We propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions. We prove that even a much smaller dataset with well-matched annotations can facilitate models to achieve better performance as well as generalizability.
  arXiv  Detail & Related papers  (2021-09-04T02:45:22Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• CoMatch: Semi-supervised Learning with Contrastive Graph Regularization [86.84486065798735]
  CoMatch is a new semi-supervised learning method that unifies dominant approaches. It achieves state-of-the-art performance on multiple datasets.
  arXiv  Detail & Related papers  (2020-11-23T02:54:57Z)
• 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)

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.