preprintOct 1, 2017Closed access

HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

Group Sense (China) · Chinese University of Hong Kong

Indexed incrossref

Abstract

Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems. Despite that the convolutional neural networks are remarkable in learning discriminative features from images, the learning of comprehensive features of pedestrians for fine-grained tasks remains an open problem. In this study, we propose a new attentionbased deep neural network, named as HydraPlus-Net (HPnet), that multi-directionally feeds the multi-level attention maps to different feature layers. The attentive deep features learned from the proposed HP-net bring unique advantages: (1) the model is capable of capturing multiple attentions from low-level to…

Citation impact

561
total citations
FWCI
22.64
Percentile
100%
References
55
Citations per year

Authors

8

Topics & keywords

Keywords
  • Pedestrian
  • Computer science
  • Discriminative model
  • Pedestrian detection
  • Generality
  • Artificial intelligence
  • Convolutional neural network
  • Deep learning
UN Sustainable Development Goals
  • Reduced inequalities
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