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Title Real Time Pedestrian Detection Using Robust Enhanced Yolov3+
ID_Doc 44268
Authors Murthy C.B.; Hashmi M.F.
Year 2020
Published Proceedings - 2020 21st International Arab Conference on Information Technology, ACIT 2020
DOI http://dx.doi.org/10.1109/ACIT50332.2020.9300053
Abstract Autonomous pedestrian detection plays a vital role in Computer Vision tasks such as smart video surveillance, smart traffic monitoring system and smart obstacle detections for building smart cities. Real-time performance is much required in self-driving cars particularly while detecting smaller pedestrians without losing any detection accuracy. The proposed paper introduces an anti-residual module in the robust Enhanced YOLOv3+ network to improve feature extraction. The proposed network is optimized by reducing bounding box loss error. This network is trained on Pascal VOC-2007+ 12 dataset, only on the extracted pedestrian images. Experimental results show this network achieves 79.86% detection accuracy while detecting smaller pedestrians and still meets the real-time requirements. © 2020 IEEE.
Author Keywords Anti-residual Module; Enhanced YOLOv3+ Network; Pascal VOC-2007+12 dataset; Pedestrian Detection


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