基于卷积神经网络优化TLD运动手势跟踪算法

针对TLD（Tracking-Learning-Detection）算法在光照变化不均、遮挡严重、跟踪目标模糊等情况下会出现跟踪失败的问题，提出一种基于卷积神经网络优化TLD运动手势跟踪算法。选取手势特征作正样本，其背景作负样本，获取手势HOG特征并投入到卷积神经网络中加以训练，得到手势检测分类器，从而确定目标手势区域，实现手势的自动识别；再利用TLD算法对手势进行跟踪与学习，对正负样本进行估计检测并实时校正，同时运用SURF特征匹配更新跟踪器。实验结果验证，该算法对比TLD经典算法跟踪精度提高了4.24%，增强了运动手势的跟踪效果，相比经典跟踪算法拥有更高鲁棒性。     

基于龙格-库塔法自适应PID控制的太阳双轴跟踪系统

论述太阳光源双轴跟踪系统的工作原理,针对其控制系统中存在的时间滞后问题提出基于龙格-库塔法的自适应PID控制。并以基于直流电机的方位角传动机构为基础,建立方位角传动机构的数学模型,在MATLAB中进行仿真。仿真结果表明,基于龙格-库塔法的自适应PID控制系统响应速度快、稳定性高,具有较强的鲁棒性,能有效解决方位角传动系统中的时间滞后问题。     

Sensory lexicon and relationships among brown colour,saltiness perception and sensory liking evaluated by regular users and culinary chefs: a case of soy sauces

This study was performed to develop lexicon to describe soy sauces using regular users (consumers) vs. culinary chefs, to perform product grouping using a free-sorting task and to determine relationship among physicochemical properties (colour, %NaCl) and consumer perception of soy sauces. Both panels identified eighteen terms to describe soy sauces, of which eleven terms (light, medium and dark brown colour, weak, medium and strong saltiness, sweetness, saltiness–sweetness, tasty/umami, and soy sauce and fermented aroma) were similar. For culinary chefs, three distinctive soy sauce groups based on brown colour (light, medium and dark brown) were identified, while two groups (light and medium dark brown) for regular users. From free-sorting maps, both panels associated brown colour intensity with saltiness expectation/perception. From PCA biplots, both panels shared some similarities for sensory liking (colour, saltiness, flavour, overall liking) for soy sauces. This study suggested that brown colour intensities affected saltiness expectation/perception and were used for product grouping for soy sauces.     

基于目标跟踪的变电站隔离开关状态图像识别方法

为解决变电站中隔离开关状态的自动识别问题，提出了基于目标跟踪的开关分合状态图像识别方法。首先，基于隔离开关开合过程的动态视频，通过 Ｌａｓｓｏ正则化稀疏表示的目标跟踪模型对隔离开关运动轨迹进行识别；其次，利用隔离开关２个闸刀之间的距离自动识别算法获得开关的分合状态。实测结果表明，该方法可快速跟踪开关运行轨迹，实时对变电站中隔离开关状态进行准确识别。     

基于船载卫星通信三轴天线的跟踪搜索技术

针对船载卫星通信三轴天线跟踪系统,介绍了船载三轴天线的系统组成,提出了一种基于捷联惯导的船载三轴天线跟踪搜索技术。天线以目标地理角为中心,通过坐标转换将目标地理角转换为天线的目标甲板角,调整AEC三轴使其及时准确地跟随目标甲板角。目前,该方法已经成功应用于多种船载三轴天线。实践表明,该方法能够快速准确地搜索到目标卫星,并实现对目标卫星的稳定跟踪。     

水下图像处理技术研究综述

随着自主式水下机器人的发展，水下探测技术成为新的研究热点。然而，吸收效应和散射效应导致水下获取的图像存在雾化和色彩偏差等缺陷。降质的水下图像在一定程度上降低了水下目标识别的准确性。为了改善水下图像质量，国内外学者对水下图像处理方法进行了深入研究。因水下图像处理方法对提升水下目标识别准确性具有良好的促进作用，故其具有重要的研究与分析价值。介绍了水下成像模型，分析了水下图像视觉质量下降的原理；根据水下物理成像模型将水下图像处理方法分为水下图像增强与水下图像复原，并分别对两类方法的研究现状进行分析与归纳；最后，总结与讨论了各类方法的优缺点，并展望了未来的发展方向。     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.     

儿童用品TBT通报数据可视化预警研究

探索采用数据可视化技术分析儿童用品TBT通报数据,以可视化图形图像呈现通报热点并揭示趋势信息,提出对策与建议,助力为儿童用品产业升级、TBT预警数据分析和信息传播工作提供新思路,提高中小企业的国外市场准入机会。