Abstract
Many object re-identification (Re-ID) methods that depend on large-scale training datasets have been proposed in recent years. However, the performance of these methods degrades dramatically when insufficient training data are available. To address this challenging problem, we propose a few-shot object re-identification (FSOR) method that enhances the generalization and discrimination abilities of object Re-ID models trained on small datasets. This method applies two novel techniques: reparameterization for feature vectors and dual-distance metric learning. The reparameterization mechanism transforms the primary feature vector of each input image into a Gaussian distribution to enhance the robustness of the FSOR method when performing object Re-ID tasks. The dual-distance metric learning technique, called H&C learning, considers both the hard mining distance and the center-point distance between each query sample and each support set of different object identities. H&C learning extracts the characteristics of the entire training dataset more precisely than other approaches and thus improves the discriminative abilities of object Re-ID models. Extensive experiments on both person and vehicle Re-ID datasets, such as Market-1501, DukeMTMC-ReID, CUHK03, and VeRi-776, show that the FSOR method has improved performance and outperforms state-of-the-art methods when the amount of labeled training data is small.
Highlights
As the demand for intelligent video surveillance has increased, object Re-identification (Re-ID), which retrieves an object of interest from a large image gallery dataset across multiple nonoverlapping cameras, has become an important computer vision task
H&C METRIC LEARNING To enhance the discriminative ability of the few-shot object re-identification (FSOR) method on the object Re-ID task, we develop the H&C metric learning method, which learns a distance metric that can precisely determine the similarities between objects
We embed the FSOR method into two existing person Re-ID models to demonstrate its ability to improve the performance of existing Re-ID models in situations with insufficient training
Summary
As the demand for intelligent video surveillance has increased, object Re-identification (Re-ID), which retrieves an object of interest from a large image gallery dataset across multiple nonoverlapping cameras, has become an important computer vision task. This task is challenging due to different camera viewpoints [1], varying image resolutions [2], illumination changes, unconstrained poses [3], image occlusion, and significant background changes. The model training step constructs a discriminative and robust Re-ID model using the annotated object images. Note that the data annotation and model training steps are invoked only during the learning phase
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.