Grid Loss: Detecting Occluded Faces
| Autor: | Michael Opitz, Georg Poier, Horst Bischof, Georg Waltner, Horst Possegger |
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| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Word error rate 02 engineering and technology 010501 environmental sciences Grid 01 natural sciences Convolutional neural network Object detection Discriminative model Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Face detection business 0105 earth and related environmental sciences |
| Zdroj: | TU Graz Computer Vision – ECCV 2016 ISBN: 9783319464862 ECCV (3) |
| Popis: | Detection of partially occluded objects is a challenging computer vision problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of the detection window are occluded, since not every sub-part of the window is discriminative on its own. To address this issue, we propose a novel loss layer for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a convolution layer independently rather than over the whole feature map. This results in parts being more discriminative on their own, enabling the detector to recover if the detection window is partially occluded. By mapping our loss layer back to a regular fully connected layer, no additional computational cost is incurred at runtime compared to standard CNNs. We demonstrate our method for face detection on several public face detection benchmarks and show that our method outperforms regular CNNs, is suitable for realtime applications and achieves state-of-the-art performance. accepted to ECCV 2016 |
| Databáze: | OpenAIRE |
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