ID 原文 译文
58778 针对现有的孪生网络目标跟踪算法存在跟踪漂移的问题,提出了一种结合深度轮廓生成网络的改进孪生网络跟踪模型,以实现复杂背景下对任何目标的稳定检测与跟踪。 The existing Siamese object tracking algorithms easily lead to tracking drift under the influence of object deformation and occlusion, this paper proposes an improved object tracking algorithm based on deep contour extraction networks to achieve stable detection and tracking of any object under complex backgrounds.
58779 首先,轮廓检测网络自动获取目标的封闭轮廓信息,并利用泛洪聚类算法获得轮廓模板; First, the contour detection network automatically obtains the closed contour information on the object and uses the flood-filling clustering algorithm to obtain the contour template.
58780 然后将轮廓模板与搜索区域输入到改进的孪生网络,获得最优跟踪评分值,并自适应地更新轮廓模板。 Then, the contour template and the search area are input into the improved Siamese network so as to obtain the optimal tracking score value and adaptively update the contour template.
58781 若目标被遮挡或跟踪丢失,则采用检测网络全视场搜索目标,实现全过程稳定跟踪。 If the object is fully obscured or lost, the Yolov3 network is used to search the object in the entire field of view to achieve stable tracking throughout the process.
58782 大量定性及定量仿真试验结果表明,这种改进模型不仅能够提高复杂背景下目标的跟踪性能,还能提升机载系统的反应时间,适合于工程应用。 A large number of qualitative and quantitative simulation results show that the improved model can not only improve the object tracking performance under complex backgrounds, but also improve the response time of airborne systems, which is suitable for engineering applications.
58783 随着人工智能在嵌入式平台上的应用,k均值聚类算法作为人工智能方法的基础,促使其在嵌入式平台上实现,而能耗是制约算法在嵌入式平台上实现的关键。为了降低k均值聚类在嵌入式平台上的能耗,提出一种针对k均值聚类的跨层精度自动调节的近似计算方法。 With the application of artificial intelligence on the embedded platform, the k-means clustering algorithm, as the basis of the artificial intelligence method, is implemented on the embedded platform. Energy consumption is the key for the algorithm implementation on the embedded platform. In order to reduce the energy consumption of the k-means on the embedded platform, an approximate computing method based on cross-layer dynamic precision scaling for the k-means is proposed.
58784 首先, 分别从数据点到质心的距离和数据点变化趋势两个方面对迭代过程进行约束,提出精度自动调节的方法; First, the iteration process is constrained from the distance between data point to centroid and data point change trend. And a dynamic precision scaling method is proposed.
58785 然后, 从结构级设计外部存储器的数据重组与访问方法,实现存储器的近似访问; Then the data reorganization and access method of external memory is designed from the structural level, which can realize the access of approximate memory.
58786 设计精度自动调节的近似加法器与乘法器,最终实现k均值聚类算法的近似计算。 In addition, the approximate adder and multiplier are designed which can automatically adjust the calculation accuracy. Finally, the approximate computing of the k-means is realized.
58787 实验结果表明,这种近似计算方法在基本不影响聚类质量的前提下,与精确计算相比较可降低55%~58%的能耗,节省能耗的比例最高。 Experimental results show that the proposed method can reduce the energy consumption by 55%~58% compared with the accurate computing without affecting the quality of clustering. The proportion of the energy saving is the highest.