Bluetooth low energy (BLE)-based indoor localization has attracted increasing interests for its low-cost, low-power consumption, and ubiquitous availability in mobile devices. In this paper, a novel denoising autoencoder-based BLE indoor localization (DABIL) method is proposed to provide high-performance 3-D positioning in large indoor places. A deep learning model, called denoising autoencoder, is adopted to extract robust fingerprint patterns from received signal strength indicator measurements, and a fingerprint database is constructed with reference locations in 3-D space, rather than traditional 2-D plane. Field experiments show that 3-D space fingerprinting can effectively increase positioning accuracy, and DABIL performs the best in terms of both horizontal accuracy and vertical accuracy, comparing with a traditional fingerprinting method and a deep learning-based method. Moreover, it can achieve stable performance with incomplete beacon measurements due to unpredictable BLE beacon lost.

Object tracking toward satellite videos faces various challenges, such as small size of the moving object, few texture, background similarities, etc. In this article, we propose a high-speed correlation filter (CF)-based tracker for object tracking on satellite videos. It takes advantage of the global motion characteristics of the moving target in satellite videos to constrain the tracking process, which is achieved by applying a Kalman filter (KF) to correct the tracking trajectory of the moving target. Thus, our tracker is named CFKF. Besides, a tracking confidence module is designed to pass information from the CF-based position detector to the KF-based trajectory corrector, and a constant optimized model update frequency is studied to speed up the tracker, as well as improve its performance. Furthermore, the target’s orientation during the tracking process can be obtained by utilizing an orientation detector based on slope calculation. Experiments conducted on the satellite video datasets demonstrate that our tracker CFKF outperforms the other representative CF-based tracking methods in terms of accuracy and robustness and is also fast in speed.

With the development of virtual reality, higher quality panoramic videos are in great demand to guarantee the immersive viewing experience. Therefore, quality assessment attaches much importance to correlated technologies. Considering the geometric transformation in projection and the limited resolution of head-mounted device (HMD), a modified display protocol of the high resolution sequences for the subjective rating test is proposed, in which an optimal display resolution is determined based on the geometry constraints between screen and human eyes. By sampling the videos to the optimal resolution before coding, the proposed method significantly alleviates the interference of HMD sampling while displaying, thus ensuring the reliability of subjective quality opinion in terms of video coding. Using the proposed display protocol, a subjective quality database for panoramic videos is established for video coding applications. The proposed database contains 50 distorted sequences obtained from ten raw panoramic video sequences. Distortions are introduced with the High Efficiency Video Coding compression. Each sequence is evaluated by 30 subjects on video quality, following the absolute category rating with hidden reference method. The rating scores and differential mean opinion scores (DMOSs) are recorded and included in the database. With the proposed database, several state-of-the-art objective quality assessment methods are further evaluated with correlation analysis. The database, including the video sequences, subjective rating scores and DMOS, can be used to facilitate future researches on coding applications.

Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the architecture of the network to the utmost degree. By utilizing the effective structural re-parameterization technique, a single convolutional layer model (SCLM) is proposed that provides global low-light enhancement as the coarsely enhanced results. In addition, we introduce a local adaptation module that learns a set of shared parameters to accomplish local illumination correction to address the issue of varied exposure levels in different image regions. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art LLIE methods in both objective metrics and subjective visual effects. Additionally, our method has fewer parameters and lower inference complexity compared to other learning-based schemes. Code will be made publicly available at the URL https://gitee.com/zhanghahaxixi/SCLM.

Band selection plays an important role in reducing the dimensionality of spectral response of hyperspectral images (HSIs) to avoid dimension disaster for land-cover classification. Compared with traditional dimension reduction methods, such as principal component analysis, independent component analysis, or linear discriminant analysis, band selection can help provide interpretability to later constructed models by preserving the physical meaning of selected features. In this letter, a group lasso-based band selection (GLBS) method is proposed for multilabel HSI classification. Using the group lasso algorithm, the two objectives of band selection and classification are implemented simultaneously. The performance of GLBS is fully investigated and compared with benchmark methods, and the experimental results demonstrate the superiority of GLBS.