| 摘要: | 摘要： In this paper, an object detector is proposed based on a convolution/subsampling feature map and a two-level cascade classifier. First, a convolution/subsampling operation alleviates illumination, rotation and noise variances. Then, two classifiers are concatenated to check a large number of windows using a coarse-to-fine strategy. Since the sub-sampled feature map with enhanced pixels was fed into the coarse-level classifier, the checked windows were drastically reduced to a quarter of the original image. A few remaining windows showing detailed data were further checked using a fine-level classifier. In addition to improving the detection process, the proposed mechanism also sped up the training process. Some features generated from the prototypes within the small window were selected and trained to obtain the coarse-level classifier. Moreover, a feature ranking algorithm reduced the large feature pool to a small set, thus speeding up the training process without losing detection performance. The contribution of this paper is twofold: first, the coarse-to-fine scheme shortens both the training and detection processes. Second, the feature ranking algorithm reduces training time. Finally, some experimental results were achieved for evaluation. From the results, the proposed method was shown to outperform the rapidly performing Adaboost, as well as forward feature selection methods.
出版者： London, England: SAGE Publications
出版日期： 2015-12-18
出處： International Journal of Advanced Robotic Systems, 2015-12, Vol.12 (12), p.1
資源來源： Openly Available Collection - Sage Journals
版權： 2015 Author(s). Licensee InTech.
版權： COPYRIGHT 2015 Sage Publications Ltd. (UK)
版權： 2015. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
識別號： ISSN: 1729-8806
識別號： ISSN: 1729-8814
識別號： EISSN: 1729-8814
識別號： DOI: 10.5772/61477 |
