We propose a dynamic simultaneous localization and mapping technology for unsupervised motion removal (UMR-SLAM), which is a deep learning-based dynamic RGBD SLAM. It is the first time that a scheme combining scene flow and deep learning SLAM is proposed to improve the accuracy of SLAM in dynamic scenes, in response to the situation where dynamic objects cause pose changes. The entire process does not require explicit object segmentation as supervisory information. We also propose a loop detection scheme that combines optical flow and feature similarity in the backend optimization section of the SLAM system to improve the accuracy of loop detection. UMR-SLAM is rewritten based on the DROID-SLAM code architecture. Through experiments on different datasets, it has been proven that our scheme has higher pose accuracy in dynamic scenarios compared with the current advanced SLAM algorithm.

Person re-identification (Re-ID) has achieved great progress in recent years. However, person Re-ID methods are still suffering from body part missing and occlusion problems, which makes the learned representations less reliable. In this paper, we propose a robust coarse granularity part-level network (CGPN) for person Re-ID, which extracts robust regional features and integrates supervised global features for pedestrian images. CGPN gains two-fold benefit toward higher accuracy for person Re-ID. On one hand, CGPN learns to extract effective regional features for pedestrian images. On the other hand, compared with extracting global features directly by backbone network, CGPN learns to extract more accurate global features with a supervision strategy. The single model trained on three Re-ID datasets achieves state-of-the-art performances. Especially on CUHK03, the most challenging Re-ID dataset, we obtain a top result of Rank-1/mean average precision (mAP)=87.1%/83.6% without re-ranking.

Among the open access standards for telecom service capabilities, Java Community Process (JCP ) and Parlay series are two mainstream standards, which provide service capability opening standards at different levels for different user objects. The JCP specifications include Java Specification Request (JSR ) 21, JSR 32, JSR 116 and JSR 289 especially for Java application developers, while Parlay brings out specifications including Parlay and ParlayX. The service capability open technologies feature different benefits and vitality due to their diversified implementations. As the community of service developers is continuously growing and the demands for integrated service development become more and more manifest, fast, effective and easy-to-use open access technologies for telecom service capabilities have become a very important research subject.