Abstract
Person re-identification receives increasing attention in recent years. However, most works assume the persons have been well cropped from the whole scene images, and only focus on learning features and metrics. This paper considers the person re-identification problem in a real-world scenario, which should consider detection and identification simultaneously. This paper proposes a multi-task learning framework for person retrieval in the wild. Person attribute learning is exploited in our framework to enhance person retrieval. Our work consists of two main contributions: (1) we present a 11 image-level attribute annotations for each image in the large-scale PRW [27] dataset, and (2) we develop an end-to-end person retrieval framework which jointly learns person detector, attribute detectors, and visual embeddings in a multi-task learning manner. We evaluate the effectiveness of the proposed approach on two tasks, i.e. person attribute recognition and person re-identification. Experimental results have demonstrated the effectiveness of the proposed approach.
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Notes
- 1.
In this work, we focus on searching the person-of-interest in a surveillance scene. We will use person and pedestrian interchangeably in the work.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grants 61632007 and 61502139, in part by Natural Science Foundation of Anhui Province under grants 1608085MF128 and in part by Anhui Higher Education Natural Science Research Key Project under grants KJ2018A0545.
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Wu, J., Zhao, Y., Liu, X. (2018). Enhancing Person Retrieval with Joint Person Detection, Attribute Learning, and Identification. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_11
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