Behaviour detection in crowded classroom scenes via enhancing features robust to scale and perspective variations. Issue 14 (9th November 2021)

• Record Type: Journal Article
• Authors: Liu, Mingyu; Meng, Fanman; Wu, Qingbo; Xu, Linfeng; Liao, Qianghua
• Is Part Of: IET image processing. Volume 15:Issue 14(2021)
• Page Start: 3466
• Page End: 3475
• Publication Date: 2021-11-09
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12318
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Detecting human behaviours in images of crowded classroom scenes is a challenging task, due to the large variations of humans in scale and pose perspective. In this paper, two modules are proposed to tackle these two variations. First, an attention‐based RoI (region‐of‐interest) extractor is designed to handle scale variation. Feature fusion and attention mechanism are used to improve the RoI feature with more local and global information. Second, a transformation‐based detection head is introduced to handle perspective variation. The spatial transformation is adopted to extract consistent representation under various perspectives. Moreover, since there is a lack of proper datasets for human behaviour detection in classroom scenes, a new dataset is created, namely CLBD. The experiments on the proposed dataset demonstrate that the modules obtain significant improvements of performance over the state‐of‐the‐art detectors.