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This paper introduces an ensemble-based self-taught learning framework for parking space classification that leverages unsupervised representation learning with convolutional autoencoders to address the challenges of limited annotated data and poor generalization across diverse environments. By employing a combination of fixed feature extractors and an ensemble of heterogeneous autoencoders, the method enhances robustness and reduces the need for extensive labeled datasets. Experimental results on the PKLot and CNRPark benchmarks demonstrate that this approach achieves impressive classification accuracies of 93% to 96% even in data-constrained scenarios, highlighting its effectiveness in real-world applications.
Achieving up to 96% accuracy in parking space classification with minimal annotated data could revolutionize intelligent transportation systems.
Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learning approaches rely on large amounts of annotated data and exhibit limited generalization across heterogeneous environments. To address these limitations, we investigate a self-taught learning framework based on unsupervised representation learning with convolutional autoencoders. The proposed approach learns transferable visual representations from unlabeled data and reuses the learned encoders as fixed feature extractors for supervised classification with limited annotated samples in the target domain. To further enhance robustness and mitigate architectural bias, an ensemble of heterogeneous autoencoders is employed, with independent classifier heads and prediction fusion at inference time. Experiments conducted on the PKLot and CNRPark benchmarks under cross-dataset evaluation protocols show that the proposed ensemble-based strategy substantially reduces annotation requirements while improving robustness under significant domain shifts, achieving accuracies between 93\% and 96\% in data-constrained scenarios.
