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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]

Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long–short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Achieving good text detection and recognition results for multi-script-oriented images is a challenging task. First, we explore bit plane slicing in order to utilize the advantage of the most significant bit information to identify text components. A new iterative nearest neighbor symmetry is then proposed based on shapes of convex and concave deficiencies of text components in bit planes to identify candidate planes. Further, we introduce a new concept called mutual nearest neighbor pair components based on gradient direction to identify representative pairs of texts in each candidate bit plane. The representative pairs are used to restore words with the help of edge image of the input one, which results in text detection results (words). Second, we propose a new idea by fixing window for character components of arbitrary oriented words based on angular relationship between sub-bands and a fused band. For each window, we extract features in contourlet wavelet domain to detect characters with the help of an SVM classifier. Further, we propose to explore HMM for recognizing characters and words of any orientation using the same feature vector. The proposed method is evaluated on standard databases such as ICDAR, YVT video, ICDAR, SVT, MSRA scene data, ICDAR born digital data, and multi-lingual data to show its superiority to the state of the art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparative evaluation of various multi-person tracking-by-detection approaches on public data sets. This paper investigates five popular trackers coupled with six relevant visual people detectors evaluated on seven public data sets. The evaluation emphasizes on exhibited performance variation depending on tracker-detector choices. Our experimental results show that the overall performance depends on how challenging the data set is, the performance of the detector on the specific data set, and the tracker-detector combination. Some trackers are more sensitive to the choice of a detector and some detectors to the choice of a tracker than others. Based on our results, two of the trackers demonstrate the best performances consistently across different data sets, whereas the best performing detectors vary per data set. This underscores the need for careful application context specific evaluation when choosing a detector. [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]

Emotion recognition is challenging due to the emotional gap between emotions and audio–visual features. Motivated by the powerful feature learning ability of deep neural networks, this paper proposes to bridge the emotional gap by using a hybrid deep model, which first produces audio–visual segment features with Convolutional Neural Networks (CNNs) and 3D-CNN, then fuses audio–visual segment features in a Deep Belief Networks (DBNs). The proposed method is trained in two stages. First, CNN and 3D-CNN models pre-trained on corresponding large-scale image and video classification tasks are fine-tuned on emotion recognition tasks to learn audio and visual segment features, respectively. Second, the outputs of CNN and 3D-CNN models are combined into a fusion network built with a DBN model. The fusion network is trained to jointly learn a discriminative audio–visual segment feature representation. After average-pooling segment features learned by DBN to form a fixed-length global video feature, a linear Support Vector Machine is used for video emotion classification. Experimental results on three public audio–visual emotional databases, including the acted RML database, the acted eNTERFACE05 database, and the spontaneous BAUM-1s database, demonstrate the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues with CNN, 3D-CNN, and DBN for audio–visual emotion recognition. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of recognizing human actions from depth videos. We propose a depth-based local descriptor and affine subspace coding representation with locality-constrained affine subspace coding (LASC) for 3D action recognition. First, each depth video sequence is divided into a set of subsequences (i.e., multi-scale sub-actions) based on the normalized motion energy vector. Next, depth motion map-based gradient local auto-correlation features are employed to capture the shape information and motion cues of each sub-action. In order to obtain discriminative and compact representation, we extract the local high-order information of the depth video using LASC. Through experiments, we show that the use of LASC exhibits better performance compared with existing methods such as locality-constrained linear coding. We compared LASC with the state-of-the-art methods based on similar principle, using features extracted from a single modality, on four datasets, and with those using multiple features or nonlinear recognition machines. The results on four datasets clearly show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Metric learning has attracted wide attention in face and kinship verification, and a number of such algorithms have been presented over the past few years. However, most existing metric learning methods learn only one Mahalanobis distance metric from a single feature representation for each face image and cannot make use of multiple feature representations directly. In many face-related tasks, we can easily extract multiple features for a face image to extract more complementary information, and it is desirable to learn distance metrics from these multiple features, so that more discriminative information can be exploited than those learned from individual features. To achieve this, we present a large-margin multi-metric learning (LM3L) method for face and kinship verification, which jointly learns multiple global distance metrics under which the correlations of different feature representations of each sample are maximized, and the distance of each positive pair is less than a low threshold and that of each negative pair is greater than a high threshold. To better exploit the local structures of face images, we also propose a local metric learning and local LM3Lmethods to learn a set of local metrics. Experimental results on three face data sets show that the proposed methods achieve very competitive results compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Individuating and locating repetitive patterns in still images is a fundamental task in image processing, typically achieved by means of correlation strategies. In this paper, we provide a solid solution to this task using a differential geometry approach, operating on Lie algebra, and exploiting a mixture of templates. The proposed method asks the user to locate a few instances of the target patterns (seeds) that become visual templates used to explore the image. We propose an iterative algorithm to locate patches similar to the seeds working in three steps: first, clustering the detected patches to generate templates of different classes, then looking for the affine transformations, living on a Lie algebra that best links the templates and the detected patches, and finally detecting new patches with a convolutional strategy. The process ends when no new patches are found. We will show how our method is able to process heterogeneous unstructured images with multiple visual motifs and extremely crowded scenarios with high precision and recall, outperforming all the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nonlinear structural hashing approach to learn compact binary codes for scalable video search. Unlike most existing video hashing methods which consider image frames within a video separately for binary code learning, we develop a multi-layer neural network to learn compact and discriminative binary codes by exploiting both the structural information between different frames within a video and the nonlinear relationship between video samples. To be specific, we learn these binary codes under two different constraints at the output of our network: 1) the distance between the learned binary codes for frames within the same scene is minimized and 2) the distance between the learned binary matrices for a video pair with the same label is less than a threshold and that for a video pair with different labels is larger than a threshold. To better measure the structural information of the scenes from videos, we employ a subspace clustering method to cluster frames into different scenes. Moreover, we design multiple hierarchical nonlinear transformations to preserve the nonlinear relationship between videos. Experimental results on three video data sets show that our method outperforms state-of-the-art hashing approaches on the scalable video search task. [ABSTRACT FROM AUTHOR]