Abstract
Pedestrian Detection in real world crowded areas is still one of the challenging categories in object detection problems. Various modern detection architectures such as Faster R-CNN, R-FCN and SSD has been analyzed based on speed and accuracy measurements. These models can detect multiple objects with overlaps and localize them using a bounding box framing it. Evaluation of performance parameters provides high speed models which can work on live stream applications in mobile devices or high accurate models which provide state-of-the-art performance for various detection problems. These convolutional neural network models are tested on the Penn-Fudan Dataset as well as Google images with occlusions, which achieves high detection accuracies on each of the detectors.
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Ajith, M., Kurup, A.R. (2018). Pedestrian Detection: Performance Comparison Using Multiple Convolutional Neural Networks. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_29
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DOI: https://doi.org/10.1007/978-3-319-96136-1_29
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