Using Boosted Features for the Detection of People in 2D Range Data
By Kai Oliver Arras, Óscar Martínez Mozos, Wolfram Burgard. Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007
433 citations recorded by the source corpus.
This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the detection of people. In particular, our approach applies AdaBoost to train a strong classifier from simple features of groups of neighboring beams corresponding to legs in range data. Experimental results carried out with laser range data illustrate the robustness of our approach even in cluttered office environments
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/5a8d931eb152d059d9c46bb64fdb9b5ea01e666f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Jia Deng
- Hei Law
- Alejandro Newell
- Luciano Spinello
- Matthias Luber
- Kai Oliver Arras
- Georgios Tzimiropoulos
- Adrian Bulat
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Xiaogang Wang
- Dacheng Tao
- Li Fei-Fei
- Ling Shao
- Andrea Vedaldi
- Andrew Zisserman
- Trevor Darrell
Citations in the corpus
- Model-free detection and tracking of dynamic objects with 2D lidar (2015, neutral): “The line of works by Arras et al. (2007, 2008) focus on people detection.”
- People detection in RGB-D data (2011, methodology): “Early works used 2D range data for this task [1], [2].”
- Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline (2021, methodology): “have relied on hand-crafted features (Arras et al., 2007), recent efforts have focused on the design of deep neural networks (DNNs) to learn features directly from raw point cloud data (Qi et al.”
- Deep tracking in the wild: End-to-end tracking using recurrent neural networks (2018, methodology): “In contrast, model-based techniques (Arras et al., 2007; Petrovskaya and Thrun, 2009; Zhao and Thorpe, 1998) explicitly consider the type of object being tracked, which limits their generality but improves tracking performance for the object classes considered.”
- People tracking in RGB-D data with on-line boosted target models (2011, methodology): “[3], where a classifier for 2D point clouds has been trained by boosting a set of geometric and statistical features.”