Dhff: Robust Multi-Scale Person Search by Dynamic Hierarchical Feature Fusion | IEEE Conference Publication | IEEE Xplore

Dhff: Robust Multi-Scale Person Search by Dynamic Hierarchical Feature Fusion


Abstract:

Person Search plays the role of the ultimate destination of person re-identification (re-ID) in real applications. It has many challenges that person re-ID doesn’t need t...Show More

Abstract:

Person Search plays the role of the ultimate destination of person re-identification (re-ID) in real applications. It has many challenges that person re-ID doesn’t need to handle, such as mis-detections, false alarms and multi-scale matching. In contrast to previous works, we show that a strong multi-scale person matching system can result in a good person search performance with a common deep object detector (e.g. Faster-RCNN). In this work, we provide a robust person search method called Dynamic Hierarchical Feature Fusion (DHFF) which is based on multi-level feature fusion to tackle with multi-scale matching. In addition, A Multi-Metric loss is proposed to train the model effectively and stably with numerous identities. We evaluate our method on two large person search benchmark data sets: CUHK-SYSU and PRW. Experiments show that the proposed algorithm outperforms other state-of-the-art person search methods.
Date of Conference: 22-25 September 2019
Date Added to IEEE Xplore: 26 August 2019
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Conference Location: Taipei, Taiwan

References

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