ID | 原文 | 译文 |
25475 | 首先应用 Paillier加密方案设计“点与区间”以及“区间与区间”关系两方保密计算基础协议,协议的特点是判定结果以密文形式输出。 | Using the Paillier encryption scheme, we design the protocols of relationship between an interval and a point (or an interval). Firstly, the outputs of protocols are ciphertexts. |
25476 | 将其推广为有理区间关系判定协议时,相比已有协议,本文协议更为安全与高效。 | If we extend it to rational intervals, the protocols are safer and more efficient than existing protocols. |
25477 | 在此基础上,进一步研究多维度的“点与区间”以及“区间与区间”关系阈值判定这一类新问题。 | And then, we study the multi-dimensional problems, that is, the threshold problems of multiple points (or intervals) and intervals, which are new problems in SMC. |
25478 | 由于基础协议的输出结果为密文,故以此为基础所设计的多维度问题协议更加安全。 | Since the outputs of the basic protocols are ciphertexts, the multi-dimensional problem protocols are more secure. |
25479 | 最后,应用模拟范例方法严格证明了协议的安全性,并对协议进行了效率分析及模拟实验,理论分析及实验结果都说明本文协议是高效的。 | We strictly prove the security of the protocols using the simulation paradigm method, analyze and demonstrate the efficiency of the protocols through experiments, and compare with the related work to illustrate that the protocols are efficient. |
25480 | 视频目标检测跟踪算法一直是计算机视觉领域的研究热点,目前大部分方法均需人工采集样本训练检测模型,搭建目标检测跟踪系统. 当目标成像条件发生变化时,需重新采集样本,训练模型,调试整个检测跟踪系统,耗费大量人力、物力。 | Video object detection and tracking algorithms have become the research focus in the field of computer vision. Traditional methods need to manually collect samples to train detection models, and build object detection and tracking systems. When the imaging conditions change, it is necessary to recollect samples to train the detection model and re-adjust the entire detection and tracking system, which requires tedious human efforts. |
25481 | 本文提出一种基于少量样本学习的多目标检测跟踪算法,只需在监控视频第一帧指定待检测目标,即可自主生成混合分类模型,进行目标检测。 | In this paper, a multi-object detection and tracking algorithm is proposed based on few-shot learning. With this approach, a hybrid classifier that models one object class can be generated by simply marking several bounding boxes around the object in the first video frame. |
25482 | 采用在线渐进学习算法学习目标姿态变化,更新该模型. 结合基于颜色的目标跟踪算法,自动构建高精度目标检测跟踪系统.整个过程无需手工采集、标注训练样本。 | An online gradual learning algorithm is proposed to learn the object pose changes and update the model. Combined with the color-based object tracking algorithm, our method automatically builds high-precision object detection and tracking systems without manual collection and labeling training samples. |
25483 | 因此,易于扩展到其它监控场景,通过自主学习形成该场景专用的检测跟踪系统,实现不同监控环境下,不同成像条件下都有较好的检测跟踪效果。 | This approach can be conveniently replicated many times in different surveillance scenes and produce scene-specific detectors under various camera viewpoints. |
25484 | 实验表明,本方法能自主学习多种监控场景下的目标姿态,无需手工标注训练样本,在基于在线学习的算法中有最佳的检测精度,此外也取得了和离线目标检测跟踪系统相似的性能。 | Experimental results on several video datasets show our approach achieves comparable performance to robust supervised methods, and outperforms the state-of-the-art online learning methods in varying imaging conditions. |