Pedestrian Detection

Pedestrian Detection

Effective techniques for human detection are of special interest in computer vision since many applications involve people’s locations and movements. Thus, significant research has been devoted to detecting, locating and tracking people in images and videos. Over the last few years the problem of detecting humans in single images has received considerable interest. Variations in illumination, shadows, and pose, as well as frequent inter- and intra-person occlusion render this a challenging task.

Software

Master’s Theses

1.Artur Jordao Lima Correia (2016): The Good, the Fast and the Better Pedestrian Detector. Federal University of Minas Gerais, 2016. (Type: Mastersthesis | Abstract | Links | BibTeX)
2.Victor Hugo Cunha de Melo (2014): Fast and Robust Optimization Approaches for Pedestrian Detection. Federal University of Minas Gerais, 2014. (Type: Mastersthesis | Abstract | Links | BibTeX)

Publications

1.Artur Jordao, Jessica de Souza Sena, William Robson Schwartz (2016): A Late Fusion Approach to Combine Multiple Pedestrian Detectors. In: IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. (Type: Inproceeding | Links | BibTeX)
2.Artur Jordao, William Robson Schwartz (2016): Oblique Random Forest based on Partial Least Squares Applied to Pedestrian Detection. In: IEEE International Conference on Image Processing (ICIP), pp. 2931-2935, 2016. (Type: Inproceeding | Links | BibTeX)
3.Artur Jordao Lima Correia (2016): The Good, the Fast and the Better Pedestrian Detector. Federal University of Minas Gerais, 2016. (Type: Mastersthesis | Abstract | Links | BibTeX)
4.Artur Jordao Lima Correia, Victor Hugo Cunha de Melo, William Robson Schwartz (2015): A Study of Filtering Approaches for Sliding Window Pedestrian Detection. In: Workshop em Visao Computacional (WVC), pp. 1-8, 2015. (Type: Inproceeding | Links | BibTeX)
5.Victor Hugo Cunha de Melo (2014): Fast and Robust Optimization Approaches for Pedestrian Detection. Federal University of Minas Gerais, 2014. (Type: Mastersthesis | Abstract | Links | BibTeX)
6.V. H. C. Melo, S. Leao, D. Menotti, W. R. Schwartz (2014): An Optimized Sliding Window Approach to Pedestrian Detection. In: IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2014. (Type: Inproceeding | Links | BibTeX)
7.William Robson Schwartz (2014): Computer Vision: A Reference Guide. In: Ikeuchi, Katsushi (Ed.): Chapter Appearance-Based Human Detection, pp. 36–38, Springer US, 2014. (Type: Inbook | Links | BibTeX)
8.W. R. Schwartz, V. H. C. de Melo, H. Pedrini, L. S. Davis (2013): A Data-Driven Detection Optimization Framework. In: Neurocomputing, 104 pp. 35-49, 2013. (Type: Article | Links | BibTeX)
9.V. H. C. de Melo, S. Leao, M. Campos, D. Menotti, W. R. Schwartz (2013): Fast Pedestrian Detection based on a Partial Least Squares Cascade. In: IEEE International Conference on Image Processing, pp. 4146 - 4150, 2013. (Type: Inproceeding | Links | BibTeX)
10.W. R. Schwartz, L. S. Davis, H. Pedrini (2011): Local Response Context Applied to Pedestrian Detection. In: Iberoamerican Congress on Pattern Recognition, pp. 181-188, 2011. (Type: Inproceeding | Links | BibTeX)
11.R. Gopalan, W. R. Schwartz (2010): Detecting Humans under Partial Occlusions using Markov Logic Networks. In: Performance Metrics for Intelligent Systems, 2010. (Type: Inproceeding | Links | BibTeX)
12.W. R. Schwartz, A. Kembhavi, D. Harwood, L. S. Davis (2009): Human Detection Using Partial Least Squares Analysis. In: IEEE International Conference on Computer Vision (ICCV), pp. 24-31, 2009, (oral presentation). (Type: Inproceeding | Links | BibTeX)
13.W. R. Schwartz, R. Gopalan, R. Chellappa, L. S. Davis (2009): Robust Human Detection under Occlusion by Integrating Face and Person Detectors. In: International Conference on Biometrics, pp. 970-979, 2009. (Type: Inproceeding | Links | BibTeX)