Attribute Aware Pooling for Pedestrian Attribute Recognition

This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm. Existing vanilla CNNs cannot be straightforwardly applied to handle multi-attribute data because of the larger label space as well as the attribute entanglement and correlations. We tackle these challenges that hampers the development of CNNs for multi-attribute classification by fully exploiting the correlation between different attributes. The multi-branch architecture is adopted for fucusing on attributes at different regions. Besides the prediction based on each branch itself, context information of each branch are employed for decision as well. The attribute aware pooling is developed to integrate both kinds of information. Therefore, attributes which are indistinct or tangled with others can be accurately recognized by exploiting the context information. Experiments on benchmark datasets demonstrate that the proposed pooling method appropriately explores and exploits the correlations between attributes for the pedestrian attribute recognition.

Authors

• Kai Han
• Yunhe Wang Huawei Noah's Ark Lab
• Han Shu University of Science and Technology of China Huawei Noah's Ark Lab
• Chuanjian Liu Huawei Technologies Ltd.
• Chunjing Xu Huawei Noah's Ark Lab
• Chang Xu University of Sydney

Keywords

• Computer Vision: Biometrics, Face and Gesture Recognition
• Computer Vision: Computer Vision
• Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
• Machine Learning: Deep Learning