Count- and Similarity-Aware R-CNN for Pedestrian Detection

Author First name, Last name, Institution

Jin Xie, Tianjin University
Hisham Cholakkal, Zayed University
Rao Muhammad Anwer, Zayed University
Fahad Shahbaz Khan, Zayed University
Yanwei Pang, Tianjin University
Ling Shao, Zayed University
Mubarak Shah, University of Central Florida

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date

1-1-2020

Abstract

© 2020, Springer Nature Switzerland AG. Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and proposal similarity information within a two-stage pedestrian detection framework. Both pedestrian count and proposal similarity are derived from standard full-body annotations commonly used to train pedestrian detectors. We introduce a count-weighted detection loss function that assigns higher weights to the detection errors occurring at highly overlapping pedestrians. The proposed loss function is utilized at both stages of the two-stage detector. We further introduce a count-and-similarity branch within the two-stage detection framework, which predicts pedestrian count as well as proposal similarity. Lastly, we introduce a count and similarity-aware NMS strategy to identify distinct proposals. Our approach requires neither part information nor visible bounding-box annotations. Experiments are performed on the CityPersons and CrowdHuman datasets. Our method sets a new state-of-the-art on both datasets. Further, it achieves an absolute gain of 2.4% over the current state-of-the-art, in terms of log-average miss rate, on the heavily occluded (HO) set of CityPersons test set. Finally, we demonstrate the applicability of our approach for the problem of human instance segmentation. Code and models are available at: https://github.com/Leotju/CaSe.

DOI Link

10.1007/978-3-030-58520-4_6

ISBN

9783030585198

ISSN

0302-9743

Publisher

Springer International Publishing

Volume

12362 LNCS

First Page

88

Last Page

104

Disciplines

Computer Sciences

Keywords

Human instance segmentation, Pedestrian detection

Recommended Citation

Xie, Jin; Cholakkal, Hisham; Muhammad Anwer, Rao; Shahbaz Khan, Fahad; Pang, Yanwei; Shao, Ling; and Shah, Mubarak, "Count- and Similarity-Aware R-CNN for Pedestrian Detection" (2020). All Works. 1107.
https://zuscholars.zu.ac.ae/works/1107

Indexed in Scopus

yes

Open Access

no