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

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)

Abstract

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.

Keywords

Pedestrian detection Human instance segmentation 

Notes

Acknowledgment

The work is supported by the National Key R&D Program of China (Grant # 2018AAA0102800 and 2018AAA0102802) and National Natural Science Foundation of China (Grant # 61632018).

Supplementary material

504472_1_En_6_MOESM1_ESM.pdf (14.9 mb)
Supplementary material 1 (pdf 15283 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Brain-Inspired Artificial Intelligence, School of Electrical and Information EngineeringTianjin UniversityTianjinChina
  2. 2.Mohamed bin Zayed University of Artificial IntelligenceAbu DhabiUAE
  3. 3.Inception Institute of Artificial IntelligenceAbu DhabiUAE
  4. 4.University of Central FloridaOrlandoUSA

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