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International Journal of Scientific Research in Computer Science, Engineering and Information TechnologyIn an era of rapid urbanization and automation, pedestrian safety has become a central concern for surveillance and smart city infrastructure. This paper presents a lightweight yet efficient system for pedestrian detection using YOLOv4-tiny, optimized ...
Rajavel JM +3 more
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YOLOv8 for Pedestrian Detection: A Comparative Study for Pedestrian Detection
2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)This research analyzes the usefulness of YOLOv8 for robust pedestrian recognition in demanding real world situations. A custom-trained YOLOv8 model is evaluated against SSD models, displaying better accuracy in detecting and localizing human classes ...
Sivaraman G, Sophiya E, Diviya M
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Confidence-Aware Fusion Using Dempster-Shafer Theory for Multispectral Pedestrian Detection
IEEE transactions on multimedia, 2023Multispectral pedestrian detection is an important and valuable task in many applications, which could provide a more accurate and reliable pedestrian detection result by using the complementary visual information from color and thermal images.
Qing Li +4 more
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Pedestrian Detection Using Multi-Scale Structure-Enhanced Super-Resolution
IEEE transactions on intelligent transportation systems (Print), 2023Pedestrian detection remains a crucial technology for applications such as autonomous driving and gait recognition and continues to be a prominent research area.
Wei-Yen Hsu, Peikun Yang
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Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving
Computer Vision and Pattern Recognition, 2023Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in realtime.
Abdul Hannan Khan, M. Nawaz, A. Dengel
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Uncertainty-Guided Cross-Modal Learning for Robust Multispectral Pedestrian Detection
IEEE transactions on circuits and systems for video technology (Print), 2022Multispectral pedestrian detection has received great attention in recent years as multispectral modalities (i.e. color and thermal) can provide complementary visual information.
Jung Uk Kim, Sungjune Park, Yong Man Ro
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Single-Pedestrian Detection Aided by Two-Pedestrian Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015In this paper, we address the challenging problem of detecting pedestrians who appear in groups. A new approach is proposed for single-pedestrian detection aided by two-pedestrian detection. A mixture model of two-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby pedestrians but cannot be captured by single ...
Wanli, Ouyang +2 more
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Attentive Alignment Network for Multispectral Pedestrian Detection
ACM Multimedia, 2023Multispectral pedestrian detection is of great importance in various around-the-clock applications, i.e., self-driving and video surveillance. Fusing the features from RGB images and thermal infrared (TIR) images to explore the complementary information ...
Nuo Chen +5 more
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IEEE transactions on multimedia, 2023
Occluded pedestrian detection is very challenging in computer vision, because the pedestrians are frequently occluded by various obstacles or persons, especially in crowded scenarios.
Chunjie Ma +4 more
semanticscholar +1 more source
Occluded pedestrian detection is very challenging in computer vision, because the pedestrians are frequently occluded by various obstacles or persons, especially in crowded scenarios.
Chunjie Ma +4 more
semanticscholar +1 more source
Improving pedestrian detection
2016 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2016Pedestrian detection is an active problem in computer vision research, with applications in robotics, self-driving cars and surveillance. It involves generating bounding boxes to indicate the location of every pedestrian in an input image. This paper proposes a method to augment a basic pedestrian detector with a Convolutional Neural Network.
Jamie Bowers, Richard Green
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