One-dimensional time series classification has always been an important research direction, playing an irreplaceable role in various fields. With the rapid development of deep learning, most traditional time series classification methods have gradually been replaced by neural network-based classification methods. At present, in the field of time series classification, models that perform well are mostly based on Convolutional Neural Networks (CNNs), while models with the Transformer architecture, which have shown outstanding performance in natural language processing and computer vision, do not stand out. In order to change this situation, We have investigated the feasibility of training models based on a single Transformer architecture directly on small datasets without additional data processing. Furthermore, we propose a new model called CTCTime, which combines the Transformer architecture with CNNs to address one-dimensional time series classification problems. We compared CTCTime with 13 traditional algorithms on 44 datasets from the UCR archive and with 7 advanced methods on 85 datasets. The UCR datasets are classic datasets for one-dimensional time series classification tasks. The experimental results demonstrate its feasibility, accuracy, and scalability.

Abstract In this paper, a new approach to visual geo‐localization for natural environments is proposed. The digital elevation model (DEM) data in virtual space is rendered and construct a panoramic skyline database is constructed. By combining the skyline database with real‐world image data (used as the “queries” to be localized), visual geo‐localization is treated as a cross‐modal image retrieval problem for panoramic skyline images, creating a unique new visual geo‐localization benchmark for the natural environment. Specifically, the semantic segmentation model named LineNet is proposed, for skyline extractions from query images, which has proven to be robust to a variety of complex natural environments. On the aforementioned benchmarks, the fully automatic method is elaborated for large‐scale cross‐modal localization using panoramic skyline images. Finally, the compound index is delicately designed to reduce the storage space of the positioning global descriptors and improve the retrieval efficiency. Moreover, the proposed method is proven to outperform most state‐of‐the‐art methods.

Abstract The information unavailability of a global navigation satellite system (GNSS) for geo‐localization in many military and tourism applications implies the need to employ other applicable types of information, such as automatic image‐based location recognition. Unlike most existing localization methods that use GNSS information to locate an initial position, this paper proposes an automatic geo‐localization framework that does not require GNSS data. The proposed framework is a two‐stage pipeline that uses a query image and digital elevation model (DEM) data as input. The authors frame automatic geo‐localization without GNSS recordings as a skyline matching problem. By extracting the skyline from the DEM data and query images, the query image can be localized by matching the query skyline feature to the DEM skyline database. It has been demonstrated that this low‐cost approach can perform efficiently in mountainous or hilly areas to produce reliable localization results. The system was tested on 50 testing site points within a large‐scale area (China, 202.6 km 2 ), and an average position error of 43.13 m was detected within 4.5 s.

Glaucoma refers to a chronic disease of the eye that leads to vision loss that is irreversible, which is called ‘silent theft of sight’. Thus, an automatic glaucoma screening pipeline from optic disc (OD) localisation to glaucoma risk prediction is proposed in this study. The proposed pipeline consists of three main phases. Firstly, the OD is localised by morphological processing and sliding window methods. Secondly, a novel neural network which is in U‐shape and convolutional introduces concatenating path and fusion loss function is developed to split OD and optic cup (OC) at the same time. Thirdly, both clinical measurements including optic cup‐to‐disc ratio (CDR), neuroretinal rim related features, and hidden features including statistical moments, entropy and energy are combined to train glaucoma classifiers. According to the results of the experiment, the proposed segmentation network achieves the best performance on both OD and OC segmentation and the proposed CDR calculation method is capable of achieving the performance similar to that of ophthalmologist on CDR measurement. Besides, the authors’ glaucoma classification model can obtain the best performance on sensitivity and area under the curve score in comparison with the existing methods.

Existing view-invariant gait recognition methods encounter difficulties due to limited number of available gait views and varying conditions during training. This paper proposes gait partial similarity matching that assumes a 3D object shares common view surfaces in significantly different views. Detecting such surfaces aids the extraction of gait features from multiple views; 3D parametric body models are morphed by pose and shape deformation from a template model using 2D gait silhouette as observation. The gait pose is estimated by a level set energy cost function from silhouettes including incomplete ones. Body shape deformation is achieved via Laplacian deformation energy function associated with inpainting gait silhouettes. Partial gait silhouettes in different views are extracted by gait partial region of interest elements selection and re-projected onto 2D space to construct partial gait energy images. A synthetic database with destination views and multi-linear subspace classifier fused with majority voting is used to achieve arbitrary view gait recognition that is robust to varying conditions. Experimental results on CMU, CASIA B, TUM-IITKGP, AVAMVG, and KY4D data sets show the efficacy of the propose method.

This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multi-view gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness.

We propose a method to recognize Chinese traffic police gesture in complex scene for intelligent vehicle. The gesture recognition is made by integrated nonparametric background modeling with human pose estimation. Firstly, dark channel prior and kernel density estimation are used to extract police's torso and arms from complex traffic environment as foreground region. Then, the coordinates of pixels in the upper and lower arms are determined by using max-covering scheme, which is based on a key observation that body part tiles maximally cover the foreground region and satisfy body plan. Finally, some typical police gestures can be recognized by rotation joint angle. The experimental results show that this method can obtain favorable results on a number of gesture sequences of traffic police.