Real-Time Pedestrian Detection with Deep Network Cascades
Abatract 我们提出一种新的实时对象检测方法，利用深层神经网络的精度和级联分类器的效率。 深层网络已被证明在分类任务方面表现优异，而且无需设计特殊的特征，他们在原始像素输入上的操作能力是非常有吸引力。然而，在inference时间，深层网络的声誉是非常低的。 在本文中，我们提出了一种将深度网络和快速特征级联的方法，既快速又准确。我们将其应用于具有挑战
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相关文章
- 1. Real-Time Pedestrian Detection With Deep Network Cascades
- 2. Crosstalk Cascades for Frame-Rate Pedestrian Detection 阅读笔记
- 3. Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
- 4. Pushing the Limits of Deep CNNs for Pedestrian Detection
- 5. Caltech Pedestrian Detection Benchmark
- 6. CUHK Occlusion Dataset (for pedestrian detection)
- 7. Deep Saliency:Multi_Task Deep Neural Network Model for Salient object detection
- 8. 【opencv】Face Detection using Haar Cascades
- 9. 【论文笔记】Malware Detection with Deep Neural Network Using Process Behavior
- 10. Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection