HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Group Sense (China) · Chinese University of Hong Kong
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
- FWCI
- 22.64
- Percentile
- 100%
- References
- 55
Authors
8Topics & keywords
- Pedestrian
- Computer science
- Discriminative model
- Pedestrian detection
- Generality
- Artificial intelligence
- Convolutional neural network
- Deep learning
- Reduced inequalities