Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection

By Yousong Zhu, Jinqiao Wang, Chaoyang Zhao, Haiyun Guo, Hanqing Lu. 2016

24 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Andreas Geiger

In Computer Vision and Pattern Recognition

1. Andreas Geiger
2. Fatma Güney
3. Luc Van Gool
4. Joel Janai
5. Aseem Behl
6. Xiaogang Wang
7. Bernt Schiele
8. Jitendra Malik

Citations in the corpus

• CoupleNet: Coupling Global Structure with Local Parts for Object Detection (2017, background): “In order to leverage the great success of deep neural networks for image classification [12, 15], considerable object detection methods based on deep learning have been proposed [9, 11, 18, 20, 29].”
• EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes (2018, methodology): “The problem of varying object sizes in pedestrian detection is tackled in the extensions [54], [55], [56], [57].”
• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Consequently, today all state-of-the-art pedestrian detection systems use convolutional neural networks and learn feature representations in an end-to-end fashion (Cai et al. (2016); Xiang et al. (2016); Zhu et al. (2016); Yang et al. (2016); Chen et al. (2015c); Ren et al. (2015)).”