Jan Hosang (PhD Student)

Personal Information
Research Interests
- Computer Vision (object recognition and localization)
- Machine Learning (deep learning)
Research Projects
- Learning non-maximum suppression
- Pedestrian detection: CVPR 2015, CVPR 2016/PAMI 2017
- Detection proposal evaluation
For more and more recent information, please visit my personal homepage (or Google Scholar, GitHub, arXiv).
Publications
2018
Towards Reaching Human Performance in Pedestrian Detection
S. Zhang, R. Benenson, M. Omran, J. Hosang and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 40, Number 4, 2018
S. Zhang, R. Benenson, M. Omran, J. Hosang and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 40, Number 4, 2018
Abstract
Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods
and the “perfect single frame detector”. We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech
pedestrian dataset). After manually clustering the frequent errors of a top detector, we characterise both localisation and background-
versus-foreground errors.
To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can
improve results even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets
for pedestrian detection, and discuss which factors affect their performance.
Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of
training and test annotations.
2017
Analysis and Improvement of the Visual Object Detection Pipeline
J. Hosang
PhD Thesis, Universität des Saarlandes, 2017
J. Hosang
PhD Thesis, Universität des Saarlandes, 2017
Abstract
Visual object detection has seen substantial improvements during the last years due to the possibilities enabled by deep learning. While research on image classification provides continuous progress on how to learn image representations and classifiers jointly, object detection research focuses on identifying how to properly use deep learning technology to effectively localise objects. In this thesis, we analyse and improve different aspects of the commonly used detection pipeline. We analyse ten years of research on pedestrian detection and find that improvement of feature representations was the driving factor. Motivated by this finding, we adapt an end-to-end learned detector architecture from general object detection to pedestrian detection. Our deep network outperforms all previous neural networks for pedestrian detection by a large margin, even without using additional training data. After substantial improvements on pedestrian detection in recent years, we investigate the gap between human performance and state-of-the-art pedestrian detectors. We find that pedestrian detectors still have a long way to go before they reach human performance, and we diagnose failure modes of several top performing detectors, giving direction to future research. As a side-effect we publish new, better localised annotations for the Caltech pedestrian benchmark. We analyse detection proposals as a preprocessing step for object detectors. We establish different metrics and compare a wide range of methods according to these metrics. By examining the relationship between localisation of proposals and final object detection performance, we define and experimentally verify a metric that can be used as a proxy for detector performance. Furthermore, we address a structural weakness of virtually all object detection pipelines: non-maximum suppression. We analyse why it is necessary and what the shortcomings of the most common approach are. To address these problems, we present work to overcome these shortcomings and to replace typical non-maximum suppression with a learnable alternative. The introduced paradigm paves the way to true end-to-end learning of object detectors without any post-processing. In summary, this thesis provides analyses of recent pedestrian detectors and detection proposals, improves pedestrian detection by employing deep neural networks, and presents a viable alternative to traditional non-maximum suppression.
2016
A Convnet for Non-maximum Suppression
J. Hosang, R. Benenson and B. Schiele
Pattern Recognition (GCPR 2016), 2016
J. Hosang, R. Benenson and B. Schiele
Pattern Recognition (GCPR 2016), 2016
Abstract
Non-maximum suppression (NMS) is used in virtually all state-of-the-art
object detection pipelines. While essential object detection ingredients such
as features, classifiers, and proposal methods have been extensively researched
surprisingly little work has aimed to systematically address NMS. The de-facto
standard for NMS is based on greedy clustering with a fixed distance threshold,
which forces to trade-off recall versus precision. We propose a convnet
designed to perform NMS of a given set of detections. We report experiments on
a synthetic setup, and results on crowded pedestrian detection scenes. Our
approach overcomes the intrinsic limitations of greedy NMS, obtaining better
recall and precision.
2015
What Makes for Effective Detection Proposals?
J. Hosang, R. Benenson, P. Dollár and B. Schiele
Technical Report, 2015
(arXiv: 1502.05082) J. Hosang, R. Benenson, P. Dollár and B. Schiele
Technical Report, 2015
Abstract
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL and ImageNet, and impact on DPM and R-CNN detection
performance. Our analysis shows that for object detection improving proposal
localisation accuracy is as important as improving recall. We introduce a novel
metric, the average recall (AR), which rewards both high recall and good
localisation and correlates surprisingly well with detector performance. Our
findings show common strengths and weaknesses of existing methods, and provide
insights and metrics for selecting and tuning proposal methods.
2014
How Good are Detection Proposals, really?
J. Hosang, R. Benenson and B. Schiele
Proceedings of the British Machine Vision Conference (BMVC 2014), 2014
J. Hosang, R. Benenson and B. Schiele
Proceedings of the British Machine Vision Conference (BMVC 2014), 2014
Abstract
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despite the popularity of detection proposals, it is unclear which trade‐offs are made when using them during object detection. We provide an in depth analysis of ten object proposal methods along with four baselines regarding ground truth annotation recall (on Pascal VOC 2007 and ImageNet 2013), repeatability, and impact on DPM detector performance. Our findings show common weaknesses of existing methods, and provide insights to choose the most adequate method for different settings.