Single-Shot Global and Local Context Refinement Neural Network for Head Detection

Jingyuan Hu () and Zhouwang Yang
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Jingyuan Hu: School of Mathematical Sciences, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei 230026, China
Zhouwang Yang: School of Mathematical Sciences, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei 230026, China

Future Internet, 2022, vol. 14, issue 12, 1-15

Abstract: Head detection is a fundamental task, and it plays an important role in many head-related problems. The difficulty in creating the local and global context in the face of significant lighting, orientation, and occlusion uncertainty, among other factors, still makes this task a remarkable challenge. To tackle these problems, this paper proposes an effective detector, the Context Refinement Network (CRN), that captures not only the refined global context but also the enhanced local context. We use simplified non-local (SNL) blocks at hierarchical features, which can successfully establish long-range dependencies between heads to improve the capability of building the global context. We suggest a multi-scale dilated convolutional module for the local context surrounding heads that extracts local context from various head characteristics. In comparison to other models, our method outperforms them on the Brainwash and the HollywoodHeads datasets.

Keywords: head detection; one-stage detector; convolutional neural network; global context; local context (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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