Towards Real-Time Person Search with Invariant Feature Learning

Publication Type:
Conference Proceeding
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, 2023-June, pp. 1-5
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Towards Real-Time Person Search with Invariant Feature Learning_published.pdfPublished version1.88 MB
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Person search aims to locate a query person in a gallery of unconstrained scene images which has many real world applications However existing methods directly build off of advances in object detection for better performance rather than efficiency Complex designs in heavy weight detectors are redundant for person search Furthermore challenges in person search force existing methods to employ additional modules which greatly deteriorates models efficiency In this paper we propose a novel real time framework for both effective and efficient person search termed as InvarPS InvarPS optimizes the over designed network with invariant feature learning Specifically considering the main challenges i e appearance changes scale variations and conflicting tasks in person search we propose an improved backbone a Single Scale Feature Fusion SSFF module and a Hierarchical Decoupling Head HDH to facilitate the model learning appearance scale and task invariant features respectively Extensive experiments demonstrate that our method achieves state of the art performance with real time speed 100 FPS which is significantly faster than any previous competitive approach
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