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• W2954156922 startingPage "2663" @default.
• W2954156922 abstract "Human detection is one of the long-standing computer vision tasks, and it has been a cornerstone for many real-world applications, such as photo album organization, video surveillance, and autonomous driving. Benefiting from deep learning technologies, such as convolutional neural networks and modern object detectors, have been achieving much improved accuracy in generic object detection tasks. In this paper, we aim to improve deep learning-based human detection. Our main idea is to exploit semantic context information for human detection by using deep-learnt semantic features provided by semantic segmentation masks. Segmentation masks play as an attention mechanism and enforce the detectors to focus on the image regions where potential object candidates are likely to appear. Meanwhile, the extra segmentation mask channel can also guide the convolutional kernels to automatically learn more discriminative features which make it easier to distinguish the background and foreground. We implement our methods with two popular detection frameworks, i.e., faster R-CNN and SSD and experimentally analyze the effectiveness of the proposed methods. Evaluation results on the widely used MS-COCO dataset and the very recent CrowdHuman dataset are provided. Our proposed methods outperform the baseline detectors and achieve better performance on highly occluded human detection." @default.
• W2954156922 created "2019-07-12" @default.
• W2954156922 creator A5003131173 @default.
• W2954156922 creator A5006294869 @default.
• W2954156922 creator A5021984184 @default.
• W2954156922 creator A5084521046 @default.
• W2954156922 date "2020-08-01" @default.
• W2954156922 modified "2023-10-11" @default.
• W2954156922 title "Human Detection Aided by Deeply Learned Semantic Masks" @default.
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• W2954156922 doi "https://doi.org/10.1109/tcsvt.2019.2924912" @default.
• W2954156922 hasPublicationYear "2020" @default.
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