Abstract
In surveillance scenarios, the pedestrian images are often facing poor resolution problems or the images are often suffered the occlusion problems. These problems make pedestrian attribute recognition more difficult. In order to solve this problem, we propose an improved pedestrian attribute recognition method based on hand-crafted feature. In this method, we use Patch Match algorithm as pedestrian image preprocessing to enhance the pedestrian images. Experiments show that this method proposed performs excellent when the pedestrian images suffer occlusion problem and the method is robust to low resolution problem.
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Acknowledgements
This research was funded by National Science and Technology Major Project (Grant number 2015ZX01041101), Jiangsu International Science and Technology Cooperation Project (Grant number BZ2017064). This research was also funded by China Scholarship Council and Jiangsu Collaborative Innovation Center of Social Safety Science and Technology.
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Zhang, Y., Wang, Q., Tang, Z. (2020). Pedestrian Attribute Recognition with Occlusion in Low Resolution Surveillance Scenarios. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_37
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DOI: https://doi.org/10.1007/978-3-030-04946-1_37
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