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Source-Free Model Transferability Assessment for Smart Surveillance via Randomly Initialized Networks.

Wei-Cheng Wang1, Sam Leroux1, Pieter Simoens1

  • 1IDLab, Department of Information and Technology, Ghent University-imec, 9052 Ghent, Belgium.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
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A new framework efficiently selects the best smart surveillance camera models for diverse environments without needing labeled data. This approach uses randomly initialized neural networks (RINNs) to ensure optimal model transferability for tasks like anomaly detection.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Smart City Technologies

Background:

  • Smart surveillance cameras are vital for automated tasks in smart cities.
  • A universal model is suboptimal due to diverse deployment environments.
  • Selecting the right model for new sites is challenging without labeled data.

Purpose of the Study:

  • To develop an automated framework for assessing model transferability in smart surveillance.
  • To identify the most suitable pre-trained model for a new deployment site.
  • To overcome the challenge of unavailable source and labeled target data.

Main Methods:

  • Constructing a model zoo of diverse environmental context models.
  • Leveraging embeddings from randomly initialized neural networks (RINNs) for task-agnostic reference embeddings.
Keywords:
randomly initialized neural networksmart surveillancetransferability assessmentunsupervised learning

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  • Quantifying embedding similarity using minibatch-Centered Kernel Alignment (CKA) without pretraining.

    • Main Results:

    • High correlations between the embedding-level score and ground-truth model rankings (Kendall's τ: 0.95, 0.94, 0.89).
    • Demonstrated consistent selection of the most transferable model across various downstream tasks.
    • Validated the framework's practicality for object tagging, anomaly detection, and event classification.

    Conclusions:

    • The proposed framework offers a robust, low-cost solution for selecting optimal surveillance models.
    • It effectively addresses the need for model adaptation or retraining in smart city deployments.
    • The method eliminates the need for labeled data, enhancing practical applicability.