- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Photovoltaic Solar Cells for Outdoor LiFi Communications
摘要： Most state-of-the-art methods in pedestrian detection are unable to achieve a good trade-off between accuracy and efficiency. For example, ACF has a fast speed but a relatively low detection rate, while checkerboards have a high detection rate but a slow speed. Inspired by some simple inherent attributes of pedestrians (i.e., appearance constancy and shape symmetry), we propose two new types of non-neighboring features: side-inner difference features (SIDF) and symmetrical similarity features (SSFs). SIDF can characterize the difference between the background and pedestrian and the difference between the pedestrian contour and its inner part. SSF can capture the symmetrical similarity of pedestrian shape. However, it for neighboring features to have such above characterization abilities. Finally, we propose to combine both non-neighboring features and neighboring features for pedestrian detection. It is found that non-neighboring features can further decrease the log-average miss rate by 4.44%. The relationship between our proposed method and some state-of-the-art methods is also given. Experimental results on INRIA, Caltech, and KITTI data sets demonstrate the effectiveness and efficiency of the proposed method. Compared with the state-of-the-art methods without using CNN, our method achieves the best detection performance on Caltech, outperforming the second best method (i.e., checkerboards) by 2.27%. Using the new annotations of Caltech, it can achieve 11.87% miss rate, which outperforms other methods.
关键词： feature extraction,non-neighboring features,Pedestrian detection,neighboring features,adaboost
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - New aspects of resonant effects in laser-modified Quantum Electrodynamics processes : (Invited)
摘要： Most state-of-the-art methods in pedestrian detection are unable to achieve a good trade-off between accuracy and efficiency. For example, ACF has a fast speed but a relatively low detection rate, while checkerboards have a high detection rate but a slow speed. Inspired by some simple inherent attributes of pedestrians (i.e., appearance constancy and shape symmetry), we propose two new types of non-neighboring features: side-inner difference features (SIDF) and symmetrical similarity features (SSFs). SIDF can characterize the difference between the background and pedestrian and the difference between the pedestrian contour and its inner part. SSF can capture the symmetrical similarity of pedestrian shape. However, it for neighboring features to have such above characterization abilities. Finally, we propose to combine both non-neighboring features and neighboring features for pedestrian detection. It is found that non-neighboring features can further decrease the log-average miss rate by 4.44%. The relationship between our proposed method and some state-of-the-art methods is also given. Experimental results on INRIA, Caltech, and KITTI data sets demonstrate the effectiveness and efficiency of the proposed method. Compared with the state-of-the-art methods without using CNN, our method achieves the best detection performance on Caltech, outperforming the second best method (i.e., checkerboards) by 2.27%. Using the new annotations of Caltech, it can achieve 11.87% miss rate, which outperforms other methods.
关键词： feature extraction,non-neighboring features,Pedestrian detection,neighboring features,adaboost
更新于2025-09-16 10:30:52