ID 原文 译文
45316 然而，目标跟踪过程中的遮挡问题会导致视频中目标信息的缺失，因此许多算法无法稳定地跟踪目标。 However, many state-of-the-art algorithms cannot track the interested object reliably due to occlusion during tracking process, which leads to deficiency of object information.
45317 针对遮挡问题，提出一种基于点轨迹的核相关滤波器跟踪算法。 In order to solve occlusion problem, a kernelized correlation tracking method based on point trajectories was proposed.
45318 通过对目标及其周围物体长期运动轨迹的估计， Through analyzing long-term motion cues of the local information, point trajectories were labeled by spectral clustering.
45319 使用谱聚类对点轨迹进行标记，以区分目标区域与背景区域，从而判断目标是否发生遮挡或漂移，决定是否启用重检测机制对目标进行检测。 These labeled points were used to differentiate the foreground and background objects and thus detect whether the target was occluded or drifts. If drifting and occlusion occur, re-detection was used to detect the re-entering of the target.
45320 实验证明，该方法对遮挡和漂移问题具有较强的顽健性。 Experimental results show that the proposed algorithm can handle occlusion and drifting problems effectively.
45321 鉴于多天线—非正交多址（MIMO-NOMA）系统中配对弱用户较差的中断性能，采用 Alamouti 码为弱用户编码，利用其分集作用来改善弱用户的中断性能，并推导出所提模型中强用户各态历经容量的闭合表达式以及弱用户各态历经容量、中断概率的边界表达式。 In view of the paired weak user’s poor outage performance in multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems, Alamouti code was adopted to encode for the weak user in order to improve its outage performance by means of diversity, and the closed-form expression of the strong user’s ergodic capacity as well as the boundary-form expressions of the weak user’s ergodic capacity and outage probability was derived in the proposed model.
45322 另外，提出了一种优化系统吞吐量的功率分配算法。 Moreover, a power allocation algorithm for optimizing the system’s throughput was proposed.
45323 最后，数值结果验证了所推表达式的准确性和所提算法的有效性，也证实了所提编码方案中弱用户的中断性能要远优于目前只采用垂直分层空时码（V-BLAST）编码方案中弱用户的中断性能。 Finally, the numerical results show the accuracy of the derived expressions, the efficacy of the proposed algorithm, and that the weak user's outage performance in the proposed coding scheme is far superior to that in the current coding scheme only adopting vertical Bell lab layered space-time (V-BLAST) code.
45324 目前行为识别发展迅速，许多基于深度网络自动学习特征的行为识别算法被提出。 Behavior recognition is developing rapidly, and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.
45325 深度学习方法需要大量数据来训练，对电脑存储、运算能力要求较高。 The deep learning method requires a large number of data to train, and requires higher computer storage and computing power.