针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中...针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像,基于其像素密度分布寻找车道线初始位置,以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明:提出的算法具有较高的准确性和实时性,算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.展开更多
随着城市化交通的发展,感知计算在智慧城市起着重要的作用。针对传统密度聚类算法无法适配海量出租车GPS轨迹数据及可视化的问题,提出了BCS-DBSCAN(Big-Data Cluster Center Statistics Density-Based Spatial Clustering of Applicatio...随着城市化交通的发展,感知计算在智慧城市起着重要的作用。针对传统密度聚类算法无法适配海量出租车GPS轨迹数据及可视化的问题,提出了BCS-DBSCAN(Big-Data Cluster Center Statistics Density-Based Spatial Clustering of Applications with Noise)聚类算法。该算法可以对轨迹数据切分及并行化聚类且能够提取最大密度簇心,并将结果适配可视化模型。实验结果表明,与其它流行的方法相比,在海量数据下提取城市载客热点区域的聚类速度、精确化及可视化方面具有十分显著的优势,对进一步提升城市规划、提高交通效率提供了重要的决策信息。展开更多
Fractional motion estimation(FME) improves the video encoding efficiency significantly. However, its high computational complexity limits the real-time processing capability. Therefore, it is a key problem to reduce t...Fractional motion estimation(FME) improves the video encoding efficiency significantly. However, its high computational complexity limits the real-time processing capability. Therefore, it is a key problem to reduce the implementation complexity of FME, especially in hardware design. This paper presents a novel deeply pipelined interpolation architecture of FME for the real-time realization of H.265/HEVC full Ultra-HD video encoder. First, a pipelined interpolation architecture together with an elegant processing order is proposed to deal with different search positions in parallel without pipeline stall and data conflict. Second, interpolation results sharing strategies are exploited among search positions to reduce the memory cost. Finally, the structure of the interpolation filter is further optimized for an area efficient implementation. As a result, the proposed design costs 41 917 slice LUTs on the Xilinx Kintex-7 FPGA platform with a 308 MHz working frequency. The measured throughput reaches a record of 1.238 Gpixels/s, which is sufficient for the real-time encoding of 8192×4320@ 30 fps video.展开更多
文摘针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像,基于其像素密度分布寻找车道线初始位置,以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明:提出的算法具有较高的准确性和实时性,算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.
文摘随着城市化交通的发展,感知计算在智慧城市起着重要的作用。针对传统密度聚类算法无法适配海量出租车GPS轨迹数据及可视化的问题,提出了BCS-DBSCAN(Big-Data Cluster Center Statistics Density-Based Spatial Clustering of Applications with Noise)聚类算法。该算法可以对轨迹数据切分及并行化聚类且能够提取最大密度簇心,并将结果适配可视化模型。实验结果表明,与其它流行的方法相比,在海量数据下提取城市载客热点区域的聚类速度、精确化及可视化方面具有十分显著的优势,对进一步提升城市规划、提高交通效率提供了重要的决策信息。
基金Supported by the Zhejiang Provincial Natural Science Foundation of China(No.LQ15F010001,LY16F020029)the General Research Project of Zhejiang Provincial Education Department(No.Y201430479)
文摘Fractional motion estimation(FME) improves the video encoding efficiency significantly. However, its high computational complexity limits the real-time processing capability. Therefore, it is a key problem to reduce the implementation complexity of FME, especially in hardware design. This paper presents a novel deeply pipelined interpolation architecture of FME for the real-time realization of H.265/HEVC full Ultra-HD video encoder. First, a pipelined interpolation architecture together with an elegant processing order is proposed to deal with different search positions in parallel without pipeline stall and data conflict. Second, interpolation results sharing strategies are exploited among search positions to reduce the memory cost. Finally, the structure of the interpolation filter is further optimized for an area efficient implementation. As a result, the proposed design costs 41 917 slice LUTs on the Xilinx Kintex-7 FPGA platform with a 308 MHz working frequency. The measured throughput reaches a record of 1.238 Gpixels/s, which is sufficient for the real-time encoding of 8192×4320@ 30 fps video.
