Semi-supervised Elastic net for pedestrian counting |
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Authors: | Ben Tan Junping Zhang Liang Wang |
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Affiliation: | 1. Department of Computer Engineering, Fars Science and Research Branch, Islamic Azad University, Shiraz, Iran;2. Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran;3. Department of Electrical Engineering, Shahid Beheshti University G.C., Tehran, Iran;4. Faculty of Computer Science and Engineering, Shahid Beheshti University G.C., Evin 1983963113, Tehran, Iran\n |
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Abstract: | Pedestrian counting plays an important role in public safety and intelligent transportation. Most pedestrian counting algorithms based on supervised learning require much labeling work and rarely exploit the topological information of unlabelled data in a video. In this paper, we propose a Semi-Supervised Elastic Net (SSEN) regression method by utilizing sequential information between unlabelled samples and their temporally neighboring samples as a regularization term. Compared with a state-of-the-art algorithm, extensive experiments indicate that our algorithm can not only select sparse representative features from the original feature space without losing their interpretability, but also attain superior prediction performance with only very few labelled frames. |
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