Improved pedestrian detection with peer AdaBoost cascade
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Improved pedestrian detection with peer AdaBoost cascade FU Hong-pu(傅红普)1, 2, 3, ZOU Bei-ji(邹北骥)1, 3, ZHU Cheng-zhang(朱承璋)1, 4, 5, DAI Yu-lan(戴玉兰)1, 5, JIANG Ling-zi(姜灵子)1, 3, CHANG Zhe(昌喆)1, 5 1. School of Computer Science and Engineering, Central South University, Changsha 410083, China; 2. School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China; 3. Hunan Province Engineering Technology Research Center of Computer Vision and Intelligent Medical Treatment, Changsha 410083, China; 4. School of Literature and Journalism, Central South University, Changsha 410083, China; 5. Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha 410083, China © Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract: Focusing on data imbalance and intraclass variation, an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed. The series of the AdaBoost classifiers are learned greedily, along with negative example mining. The complexity of classifiers in the cascade is not limited, so more negative examples are used for training. Furthermore, the cascade becomes an ensemble of strong peer classifiers, which treats intraclass variation. To locally train the AdaBoost classifiers with a high detection rate, a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds. Using the aggregate channel feature (ACF), the method achieves miss rates of 35% and 14% on the Caltech pedestrian benchmark and Inria pedestrian dataset, respectively, which are lower than that of increasingly complex AdaBoost classifiers, i.e., 44% and 17%, respectively. Using deep features extracted by the region proposal network (RPN), the method achieves a miss rate of 10.06% on the Caltech pedestrian benchmark, which is also lower than 10.53% from the increasingly complex cascade. This study shows that the proposed method can use more negative examples to train the pedestrian detector. It outperforms the existing cascade of increasingly complex classifiers. Key words: peer classifier; hard negative refining; pedestrian detection; cascade Cite this article as: FU Hong-pu, ZOU Bei-ji, ZHU Cheng-zhang, DAI Yu-lan, JIANG Ling-zi, CHANG Zhe. Improved pedestrian detection with peer AdaBoost cascade [J]. Journal of Central South University, 2020, 27(8): 2269−2279. DOI: https://doi.org/10.1007/s11771-020-4448-1.
1 Introduction As a popular topic in computer vision, pedestrian detection can be used to assist various applications [1, 2], such as human gait recognition, human identification, traffic surveillance [3] and
vehicle navigation. The detection problem is challenging, due to appearance variation between pedestrian examples in different poses, clothes, scales, viewpoints, and other situations and data imbalance. Numerous models have been proposed for pedestrian detection. Rigid models [4, 5] express
Foundation item: Project(2018AAA0102102) supported by the National
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