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Deep learning methods, especially CNNs, have achieved many promising results in a wide range of computer vision applications. However, few studies focused on designing suitable deep learning methods for parking space status inference. As we have known, it is challenging to detect parking spaces in an outdoor environment due to dynamic lighting variations, weather changes, and perspective distortion. By off-the-shelf CNNs, lighting variations might be handled well. However, to realize a practical and robust inference system, we also need to address troublesome problems, such as parking displacements, non-unified car sizes, inter-object occlusion, and perspective distortion. These problems may become even challenging if also considering the difference of space sizes. To overcome the problems, we proposed a custom–tailored deep convolutional and contrastive network with three contributions. First, we introduced a Siamese architecture to learn the contrastive and robust feature descriptor. This helps to reduce the effects owing to the variety of inter-object occlusion. Second, we integrated a convolutional Spatial Transformer Network (STN) to adaptively transform a 3-space input patch according to vehicle sizes and parking displacement. STN also helps to overcome the perspective distortion problem. Third, a multi-task loss function was designed to train the network by simultaneously considering the accuracy of inferring the status of the target space and the semantic smoothness of high-level features. Thereby, the errors caused by inter-object occlusion could be alleviated. To verify the proposed network, we visualized the learned features and analyzed their functionality. Experiments and evaluations have shown the robustness of our system in parking status inference. The real-time system currently running in public parking lots also demonstrates the effectiveness of the proposed deep network. [ABSTRACT FROM AUTHOR]

Linear discriminant analysis (LDA) is a very popular supervised feature extraction method and has been extended to different variants. However, classical LDA has the following problems: 1) The obtained discriminant projection does not have good interpretability for features; 2) LDA is sensitive to noise; and 3) LDA is sensitive to the selection of number of projection directions. In this paper, a novel feature extraction method called robust sparse linear discriminant analysis (RSLDA) is proposed to solve the above problems. Specifically, RSLDA adaptively selects the most discriminative features for discriminant analysis by introducing the $l_{2,1}$ norm. An orthogonal matrix and a sparse matrix are also simultaneously introduced to guarantee that the extracted features can hold the main energy of the original data and enhance the robustness to noise, and thus RSLDA has the potential to perform better than other discriminant methods. Extensive experiments on six databases demonstrate that the proposed method achieves the competitive performance compared with other state-of-the-art feature extraction methods. Moreover, the proposed method is robust to the noisy data. [ABSTRACT FROM AUTHOR]

High-dimensional data contain not only redundancy but also noises produced by the sensors. These noises are usually non-Gaussian distributed. The metrics based on Euclidean distance are not suitable for these situations in general. In order to select the useful features and combat the adverse effects of the noises simultaneously, a robust sparse subspace learning method in unsupervised scenario is proposed in this paper based on the maximum correntropy criterion that shows strong robustness against outliers. Furthermore, an iterative strategy based on half quadratic and an accelerated block coordinate update is proposed. The convergence analysis of the proposed method is also carried out to ensure the convergence to a reliable solution. Extensive experiments are conducted on real-world data sets to show that the new method can filter out the outliers and outperform several state-of-the-art unsupervised feature selection methods. [ABSTRACT FROM AUTHOR]