2021
DOI: 10.1109/tits.2019.2963700
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A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

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Cited by 52 publications
(31 citation statements)
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“…The aforementioned feature fusion structures play a great role in generic object detection. Some works like [67], [102], [104], [113], [114] borrow from these ideas and propose some new fusion strategies to adapt to pedestrian detection. Some typical frameworks are shown in Figure 13.…”
Section: A Leverage Multi-scale Feature Fusionmentioning
confidence: 99%
“…The aforementioned feature fusion structures play a great role in generic object detection. Some works like [67], [102], [104], [113], [114] borrow from these ideas and propose some new fusion strategies to adapt to pedestrian detection. Some typical frameworks are shown in Figure 13.…”
Section: A Leverage Multi-scale Feature Fusionmentioning
confidence: 99%
“…Repulsion loss [ 16 ], for example, works by setting the loss function forecasting responsible for the distance of the frame of objects, and, together with the surroundings, is not the actual target box (the box that contains natural objects and predict boxes) of space used to improve the model performance. Yang proposed a partially sensing multi-scale fully convolutional network to solve these occlusion and large-scale problems [ 17 ]. The most responsive part is selected by voting, and partially visible pedestrian instances can obtain a high detection confidence value, making it unlikely to miss detection.…”
Section: Realted Workmentioning
confidence: 99%
“…However, people can suffer from occlusion as well as variations in illumination, scale, and background, which make human detection in indoor scenes a challenging task. Methods based only on RGB features [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] can no longer meet the needs of human detection in many scenarios. With the popularization of inexpensive depth acquisition equipment, detecting human with the help of depth information has become an effective and feasible scheme.…”
Section: Introductionmentioning
confidence: 99%
“…To address the challenges of occlusion and scale changes in RGB images, several pedestrian detection algorithms [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ] have been developed based on novel processing approaches. Andre et al [ 7 ] proposed a cascaded aggregate channel features (ACF) detector to accurately detect humans.…”
Section: Introductionmentioning
confidence: 99%
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