基于改进YOLOv3的智慧足球场行人检测

由于足球比赛场景中密集人群、移动小目标居多, YOLOv3算法存在检测精确度较低且模型参数量较大等问题, 使其无法部署在资源算力有限的移动设备上, 本文提出了一种基于改进YOLOv3的行人检测方法, 将Darknet-53主干特征提取网络替换为更加高效且轻量化的GhostNet网络; 同时选取了4个尺度的检测分支层并采用K-means++算法改善anchor box的聚类效果; 添加空间金字塔池化对输入图像实现相同大小的输出; 提出CIoU损失函数来计算目标定位损失值; 添加heatmap热力图可视化并在训练中使用Mosaic数据增强. 实验结果表明, YOLOv3-GhostNet在VOC融合数据集上mAP达到90.97%的同时相比YOLOv3算法提高了1.75%, 参数量减少了约81.4%且实时检测速率提高了约1.5倍, 在小型移动设备上表现出不错的检测效果.     

Label distribution learning for scene text detection

Recently, segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved texts. However, existing methods usually need complex post-processing stages to process ambiguous labels, i.e., the labels of the pixels near the text boundary, which may belong to the text or background. In this paper, we present a framework for segmentation-based scene text detection by learning from ambiguous labels. We use the label distribution learning method to process the label ambiguity of text annotation, which achieves a good performance without using additional post-processing stage. Experiments on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection.     

Saliency guided self-attention network for pedestrian attribute recognition in surveillance scenarios

Pedestrian attribute recognition is often considered as a multi-label image classification task. In order to make full use of attribute-related location information, a saliency guided sel-attention network ( SGSA-Net) was proposed to weakly supervise attribute localization, without annotations of attribute-related regions. Saliency priors were integrated into the spatial attention module ( SAM ). Meanwhile,channel-wise attention and spatial attention were introduced into the network. Moreover, a weighted binary cross-entropy loss ( WCEL) function was employed to handle the imbalance of training data. Extensive experiments on richly annotated pedestrian ( RAP) and pedestrian attribute ( PETA) datasets demonstrated that SGSA-Net outperformed other state-of-the-art methods.     

孪生网络框架下融合显著性和干扰在线学习的航拍目标跟踪算法

针对一般跟踪算法不能很好地解决航拍视频下目标分辨率低、视场大、视角变化多等特殊难点,该文提出一种融合目标显著性和在线学习干扰因子的无人机(UAV)跟踪算法.通用模型预训练的深层特征无法有效地识别航拍目标,该文跟踪算法能根据反向传播梯度识别每个卷积滤波器的重要性来更好地选择目标显著性特征,以此凸显航拍目标特性.另外充分利用连续视频丰富的上下文信息,通过引导目标外观模型与当前帧尽可能相似地来在线学习动态目标的干扰因子,从而实现可靠的自适应匹配跟踪.实验证明:该算法在跟踪难点更多的UAV123数据集上跟踪成功率和准确率分别比孪生网络基准算法高5.3%和3.6%,同时速度达到平均28.7帧/s,基本满足航拍目标跟踪准确性和实时性需求.     

基于学习主动中心轮廓模型的场景文本检测

在场景文本检测领域,存在由于文本尺寸波动较大导致的小文本漏检、大文本欠检测和多尺度文本边界检测错误的情况。针对上述问题,提出一种基于学习主动中心轮廓模型的场景文本检测网络。在残差网络ResNet的基础上构建多尺度特征权重融合模型,对输入的场景文本图片进行多尺度特征提取和权重融合,并计算出最终的特征融合图,适应场景文本长宽比变化较大的情况。在此基础上,将融合后的特征图输入到学习主动中心轮廓模型预测文本框的中心点和边界,该模型为场景文本检测提供丰富先验知识,以解决多尺度文本检测框包含过多背景或部分包围文本造成的边界检测错误问题。在MSRA-TD500、IC13、IC15和IC17MLT数据集上的实验结果表明,该网络能够提高多尺度场景文本检测的准确率,其中在MSRA-TD50数据集上F-measure为0.83,相较于MSR方法提升1%,在IC13数据集上F-measure为0.91,相较于PixelLink网络提升2%,在IC15数据集上F-measure值为0.87,相较于PSENet网络提升1%,在IC17MLT数据集上F-measure值为0.74,相较于TridentNet网络提升1%。     

设计语用学的范式研究

目的确定设计语用学研究范畴,建立设计语用学的研究范式,进而通过设计语用学理论发展对设计方法论提供新的理论依据。方法从设计符号传播语境视角出发,对设计方、用户和用户的社交对象之间的设计符号传播模式进行论述。结论结合整合营销传播理论和设计事理学理论,得出了设计语用学的研究范畴、研究意义、研究核心及整合产品设计营销传播与定义设计符号使用场景两种设计语用学的研究路径,完成了设计语用学的理论框架。     

Rational Filters for Passive Depth from Defocus

A fundamental problem in depth from defocus is the measurement of relative defocus between images. The performance of previously proposed focus operators are inevitably sensitive to the frequency spectra of local scene textures. As a result, focus operators such as the Laplacian of Gaussian result in poor depth estimates. An alternative is to use large filter banks that densely sample the frequency space. Though this approach can result in better depth accuracy, it sacrifices the computational efficiency that depth from defocus offers over stereo and structure from motion. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. In addition, a depth confidence measure is derived that can be computed from the outputs of the operators. This confidence measure permits further refinement of computed depth maps. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.51.2% of the distance of the object from the imaging optics.     

城市中心步行化空间探讨

二次世界大战后欧洲城市开始进行步行街建设,进入７０年代步行街在西方国家大量流行。我国在９０年代开始进行城市步行街的规划建设。本文在总结国内外步行街发展的原因以及城市中心步行环境的类型的基础上,提出城市中心步行空间规划设计要点。     

公安网络安全技术地教学研究

文章以培养学生网络攻击案件侦查、取证能力为目标,对公安院校《网络安全技术》课程教学方法、教学内容进行了研究.文中通过具体实例介绍了从Web服务器日志、网络通信数据、被攻击主机内存中提取、分析入侵线索的方法,同时介绍了“主题、情景式”、“案例式”和“启发式”教学方法在实验教学中的具体运用.     

基于OSG的虚拟场景漫游技术研究

虚拟场景是虚拟现实应用中必不可少的部分,但是由于其构成的复杂性使得对它的管理和编辑非常困难。文章利用OSG（OpenSceneGraph）提供的虚拟场景管理和图形渲染优化的特点,提出实现漫游设计和碰撞检测的方法。