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15 March 2019 Occlusion-aware pedestrian detection
Christos Apostolopoulos, Kamal Nasrollahi, M. Hsuan Yang, Mohammad N. S. Jahromi, Thomas B. Moeslund
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Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110410I (2019) https://doi.org/10.1117/12.2523107
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
Abstract
Failure in pedestrian detection systems can be extremely crucial, specifically in driverless driving. In this paper, failures in pedestrian detectors are refined by re-evaluating the results of state of the art pedestrian detection systems, via a fully convolutional neural network. The network is trained on a number of datasets which include a custom designed occluded pedestrian dataset to address the problem of occlusion. Results show that when applying the proposed network, detectors can not only maintain their state of the art performance, but they even decrease average false positives rate per image, especially in the case where pedestrians are occluded.
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Christos Apostolopoulos, Kamal Nasrollahi, M. Hsuan Yang, Mohammad N. S. Jahromi, and Thomas B. Moeslund "Occlusion-aware pedestrian detection", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410I (15 March 2019); https://doi.org/10.1117/12.2523107
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KEYWORDS
Sensors
Image segmentation
Convolutional neural networks
Image resolution
Binary data
Sensor performance
Failure analysis
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