【精品文档】05中英文双语毕设外文翻译成品：V2V协同环境中对智能车辆进行快速行人检测和动态跟踪

此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子也就一次的事！外文标题：Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment外文作者：Fuliang Li, Ronghui Zhang , Feng You文献出处：Iet Image Processing , 2018 , 11 (10) :833-840(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文2203单词， 14998字符(字符就是印刷符)，中文3668汉字。原文：Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environmentFuliang Li, Ronghui Zhang , Feng YouAbstract: Pedestrian detection has become one of the hottest topics in intelligent traffic system because of its potential applications in driver assistance and automatic driving. In this study, a fast pedestrian detection and dynamic tracking method within vehicle-to-vehicle (V2V) cooperative environment is proposed. A dynamic tracking-by-detection framework for real-time pedestrian detection is developed. First, a cascade classifiers, based on selected Haar-like features, is trained to detect pedestrian. Then, CamShift algorithm combined with extended Kalman filtering is used to pedestrian dynamic tracking. Finally, with the crowdsourcing detected information, a smartphone-based V2V cooperative warning system is developed to share useful detection results within blind spots. The experiment results show that the proposed method has a real-time and accurate performance, which can provide a reference for road traffic safety monitoring technology.IntroductionIn recent years, pedestrian deaths resulting from the complex traffic environment accounted for 60% of all deaths on the roads 1. Aiming to reduce collision and danger to pedestrians from traffic, pedestrian active safety analysis has become an international research focus, especially pedestrian detection technology.In general, pedestrian detection methods can be divided into target characteristics template-based and pedestrian-based learning methods. The former type of methods cost less and are relatively simple. However, those methods only work well by detected obvious contour, and their detection effects have a direct relationship with template choice. Davis and Mark 2 proposed a two-step template method based on infrared images. Detection results are correlated with the selected template directly. A pedestrian gait pattern based template detection method is proposed by Bertozzi et al. 3. The method first calculates human probability template based on pedestrian gait pattern, and then determines whether the object is a pedestrian or not using calculated joint probability. This method is applicable to detect pedestrian with leg visible. Zhuang and Liu 4 put forward a probability template-matching algorithm to realise pedestrian detection. The method uses the local double segmentation threshold to extract candidate targets and traverse the multi-scale probability template. This method requires fewer samples but the error rate is higher in complex urban road environment, and real-time performance is poor.Pedestrian tracking is expected to predict information such as pedestrians position in the next few frames based on the detection information in the current frame. In general, continuous detection can be replaced by pedestrian tracking for enhancing the pedestrian detections real-time performance 14. Probability-based pedestrian tracking method is a research hotspot for solving tracking problems. Without loss of generality, pedestrian tracking can be treated as a state estimation issue. The Kalman filtering and the particle filter tracking method are common methods in this field 15. Liu et al. 16 proposed a CamShift moving-target tracking algorithm, using the extended Kalman filter to estimate targets motion speed and spatial location. Li et al. 17 developed an adaptive Kalman filter tracking algorithm, which modify the statistical model of the filter in real time and apply the least squares SVM to estimate target moving direction. Wang and Tang 18 proposed a particle filter pedestrian tracking method using piecewise Gaussian model, which applies piecewise Gaussianmodel based probability distribution to estimate pedestrian maximum likelihood moving direction directly.The key contributions of this proposed method include: a dynamic tracking-by-detection method for real-time pedestrian detection, which means that selected Haar-like features based cascade classifiers are proposed to detect pedestrian first, and then make a dynamic pedestrian tracking using CamShift algorithm combined with extended Kalman filtering (EKF). A smart phone based V2V cooperative warning system is developed to share useful detection results within blind spots.Selected Haar-like based cascade classifiers for fast pedestrian detectionSelected Haar-like features and weak classifier trainingHaar-like features are defined as the differences in the greyscale sum of black and white rectangles corresponding regions in the image sub-window, which can extract image texture features effectively. For pedestrian detection, the most frequent changes of greyscale are in the vertical and horizontal directions. Thus, this paper selects global eight types or local human shape rectangular feature shown in Fig. 1, from which the differences of characteristics in appearance can be highlighted effectively.Haar-like features can be computed rapidly using intermediate representation called the integral image 22. The integral image at location (x, y), denoted by ii(x, y), contains the sum of the pixel values above and to the left of (x, y) shown in Fig. 2a, which canbe calculated bywhere I(x, y) is the original image that can be obtained using following iterative calculations:Thus, the integral image can be computed in just one pass over the original image, which means that we can calculate the value of selected Haar-like features rapidly. Take four array references shown in Fig. 2b as an example, the integral image value at location 1 for the regional grey level A sum is denoted by A. Correspondingly, the values of locations 2, 3, and 4 are A + B, A + C, and A + B + C + D. Finally, the sum within rectangular region D can be expressed as 4 + 1(2 + 3).During the classifiers training process, an improved Adaboost algorithm was used to process weak classifiers training based on minimum error rate principle, which can reduce the weight of samples with the correct classification, but increase the weight of samples with the wrong classification. More precisely, the weak offline trained classifiers can decrease the error rate and shorten the training time at the same time. Then, strong classifiers can be obtained, which are composed of several weak classifiers using linear superposition. More details about the training process can refer to our previous research work 5.Design of the cascade classifierCascade structure, a kind of degeneration decision tree, focuses on processing the key image region 23. Each strong classifier search window is moved across the input target and check whether there is pedestrian or not. Only the input target passing through all strong classifiers can be considered as pedestrian. The flowchart of cascade classier is shown in Fig. 3.V2V cooperative warning platformThere are some typical blind spots in urban traffic conditions, such as turning, lane-changing areas at intersection or merging sections, where the above pedestrian detection and tracking methods cannot work effectively. In this case, using V2V communication is one of ways to share pedestrian warning information out of individual vehicle detection range. Based on those timely cooperative warning information, the driver can make more reliable decisions and has a better chance of reacting properly emergency situations. However, there are several challenges during the pedestrian cooperative warning process. The first is position interruption, which is the distance between the locations positioned by global positioning system (GPS) receiver and the actual location. Inaccurate sensor information leads to uncertain vehicle or pedestrian state information that influences the cooperative warning system performance. Another challenge is redundant information, which means that there is the possibility of overloading the driver with too much warning information.To reduce pedestrian collision probability, we propose a novel cooperative warning framework to share pedestrian warning information within V2V cooperative environment. Detected pedestrian warning information, including pedestrians position, speed, and movement, can be shared to related vehicles in blind spots. The above position interruption and redundant information are two significant issues which this framework has tried to deal with. To reduce the impact of position interruption, the framework develops a double GPS positioning system using carrier phase differential technology to improve positioning accuracy on one hand and make an optimisation for broadcasting interval on the other hand 27. More details are discussed in Section 5. A tradeoff mechanism is proposed between a successful warning and the risk disturbing drivers to release redundant information. Without loss of generality, we assume that the vehicle traveling route information is known. Thus, the tradeoff issue can be transferred into a cluster optimisation problem. Based on vehicle GPS and pedestrian detection and tracking information, the backstage information processing center (BIPC) first classify all vehicles in blind spots into potential collision and collision-free vehicles. Then, the BIPC make different degree warning information for potential vehicles based on collision probability. The flowchart of the proposed framework is shown in Fig. 6.More precisely, the application scenario description is as follows: a host vehicle detects and tracks pedestrians using the above proposed fast pedestrian detection and dynamic tracking method, which was introduced in Sections 2 and 3. Then its GPS data and pedestrian GPS data estimated by single-frame static image distance model are sent to the BIPC via 4G network 28. After that, the BIPC determines the pedestrian speed and moving direction through analysing pedestrian GPS data sequences first. Then the BIPC make a cluster of surrounding vehicles in blind spots into potential collision and collision-free vehicles. Further collision probabilities are calculated by potential collision vehicle and pedestrian GPS data. Finally, the BIPC broadcasts different degree timely warning information to potential collision vehicles.ExperimentsTo validate the proposed method, we develop a system using Visual 2010 and Intel OpenCV to test it. Then, it is transplanted to the HUAWEI GlORY mobile phone. It has hardware configurations of Hisilicon Kirin 935 + 3GB RAM + 2000W BSI camera to realise fast pedestrian detection. All test equipments were installed into a Chery Tiggo NCV vehicle shown in Fig. 7.ConclusionIn this paper, we present a fast pedestrian detection and dynamic tracking method for intelligent vehicles within V2V cooperative environment. The key contributions of this proposed method include: a dynamic tracking-by-detection method for real-time pedestrian detection, which means that selected Haar-like features based cascade classifiers are proposed to detect pedestrian first. Then in view of the non-linear characteristics of urban road conditions, a dynamic pedestrian tracking using CamShift algorithm combined with EKF is developed to improve the real- time performance of pedestrian detection. Also, a smartphone-based V2V cooperative warning system is developed to share useful detection results within blind spots, which reduce single vehicles blind spots and lower accident rates at the intersection area. The road experiment results show that the proposed fast pedestrian detection method has a more robust, higher performance compared with other state-of-the-art methods.However, this paper cannot analyse the effect of bad weather (such as rain, fog day) and vehicle speed on the algorithms performance and reliability 32 due to experiment environment lmitation. All of those will be the focus of our future research work.References1 Zhang, S., Christian, B., Armin, B.C.: Efficient pedestrian detection via rectangular features based on a statistical shape model, IEEE Trans. Intell. Transp. Syst., 2015, 16, (2), pp. 763-7752 Davis, J.W., Mark, A.K.: A two-stage template approach to person detection in thermal imagery, IEEE Workshop Motion Video Comput., 2005, 2005, pp. 364-3693 Bertozzi, M., Broggi, A., Del, R.M., et al.: A pedestrian detector using histograms of oriented gradients and a support vector machine classifier. IEEE Conf. on Intelligent Transportation Systems, 2007, pp. 143-1484 Zhuang, J., Liu, Q.: Nighttime pedestrian detection method for driver assistance systems, J. South China Univ. Technol., 2012, 40, (8), pp. 56-625 Li, F., You, F., Zhang, R., et al.: An improved real-time detection and localization scheme for pedestrian based on information fusion, Int. J. Appl. Math. 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IEEE Conf. on Computer Vision and Pattern Recognition, 2013, pp. 947-954译文：V2V协同环境中对智能车辆进行快速行人检测和动态跟踪Fuliang Li, Ronghui Zhang , Feng You摘要在智能交通系统中，行人检测已经成为最受热议的话题之一，这是因为它在驾驶辅助和自动驾驶中具有潜在的应用。在本次的研究中，我们提出了车辆间（V2V）协同环境中的快速行人检测和动态跟踪方法。本文开发了一种用于实时行人检测的动态跟踪检测框架。首先，对具有选定的Haar-like特征的级联分类器进行训练以检测行人。 然后，结合CamShift算法与拓展的卡尔曼滤波来用于行人动态跟踪。 最后，利用众包检测信息，开发了基于智能手机的V2V合作预警系统，其可以以在盲区内共享有用的检测结果。 实验结果表明，该方法具有实时性和准确性，可为道路交通安全监测技术提供参考。引言近年来，由于复杂的交通环境造成的行人死亡占道路死亡人数的601。 为了减少交通行人碰撞以及危险，交通路面行人安全分析已成为国际研究热点，特别是行人检测技术。一般来说，行人检测方法可以分为基于目标特征和基于了解行人行为的方法。前一种方法成本较低，而且相对简单。然而，这些方法只能检测到一般的轮廓，并且其检测效果与模板选择有直接关系。 Davis和Mark 2提出了基于红外图像的两步模板方法。检测结果直接与选定的模板相关联。 Bertozzi等人提出了一种基于步行模式的检测方法3。该方法首先基于行人步态模式计算人体概率模板，然后使用计算的联合概率确定物体是否为行人。该方法适用于检测可见腿部的行人。 Zhuang和Liu 4提出了一种概率模板匹配算法来实现行人检测。该方法使用局部双分割阈值来提取候选目标并运用多尺度概率模板。这种方法需要的样本较少，但在复杂的城市道路环境中误差率较高，实时性较差。在当前的框架中基于监测的信息，行人跟踪系统将被用于预测诸如行人在接下来几帧的位置等信息。一般来说，连续性检测可以被行人追踪系统所取代，以提高行人检测的实时性14。基于概率的行人跟踪方法是解决跟踪问题的研究热点。在不失其普遍性的情况下，行人追踪可被视为行人状态预测的问题。卡尔曼滤波和粒子滤波跟踪方法是该领域的常用方法15。刘等人16提出了CamShift运动目标跟踪算法，使用扩展的卡尔曼滤波来估计目标的运动速度和空间位置。 Li等人17开发了一种自适应卡尔曼滤波跟踪算法，实时修改滤波器的统计模型，并应用最小二乘支持向量机来估计目标移动方向。 王和唐18提出了一种采用分段高斯分段模型的粒子滤波行人跟踪方法，这是基于模型的概率分布直接估计行人最大似然移动方向。图一 选定的Haar-like特征图二 积分图计算提出的这种方法主要意义包括：它是一种实时行人检测的动态跟踪检测方法，这意味着具有选定的Haar-like特征的级联分类器被首次提出来检测行人，然后使用动态行人跟踪 CamShift算法并结合扩展的卡尔曼滤波（EKF）， 开发了基于智能手机的V2V协作预警系统，以在盲点内共享有用的检测结果。具有选定的Haar-like特征的级联分类器用于快速行人检测选定的Haar-like特征以及弱分类器的训练Haar-like特征可以定义为图像子窗口中黑白矩形对应区域灰度等级的差异，其可以有效提取图像纹理特征。 对于行人检测，最常见的灰度变化是垂直和水平方向。 因此，本文选取了图1所示的全局八种类型或局部人体形状矩形特征，从中可以有效地突出表现出特征差异。可以使用被称之为积分图像的中间表达式快速计算类Haar-like特征22。积分图像位置为（x，y），可以表达为ii(x, y),包含在图2a中示出的（x，y）的上方和左侧的像素值的总和，其可以计算为：其中I（x，y）是可以使用以下迭代计算获得的原始图像：因此，积分图像可以在原始图像上仅仅计算一次，这意味着我们可以快速计算选择的Haar-like特征值。 以图2b中所示的四个阵列参考为例，区域灰度A sum的位置1处的积分图像值由A表示。相应地，位置2,3和4的值是A + B，A + C和A + B + C + D。最后，矩形区域D内的和可以表示为4 + 1-（2 + 3）。在分类器训练过程中，基于最小误差率原理，可以采用改进的Adaboost算法来处理弱分类器训练，这可以减少正确分类样本的权重，但也会增加分类错误的样本权重。 更准确地说，弱离线训练分类器可以同时降低错误率并缩短训练时间。 然后，可以获得强分类器，它是由几个使用线性叠加的弱分类器组成的。 关于训练过程的更多细节可以参考我们以前的研究工作5。在分类器训练过程中，采用改进的Adaboost算法处理基于最小误差率原理的弱分类器训练，可以减少正确分类样本的权重，但增加分类错误的样本权重。 更准确地说，弱离线训练分类器可以同时降低错误率并缩短训练时间。 然后，可以获得强分类器，它是由几个使用线性叠加的弱分类器组成的。 关于培训过程的更多细节可以参考我们以前的研究工作5。级联分类器的设计级联结构是一种退化决策树的算法，重点是处理关键图像区域23。 每个强分类器搜索窗口在输入的目标上移动并检查是否存在行人。 只有通过所有强分类器的输入的目标才能被视为行人。 级联分类器的流程图如图3所示。图三 级联分类器的设计V2V 协同预警平台在城市交通道路中存在一些常见的盲点，如转弯、交叉口车道变换区或合并路段等，上述的行人检测跟踪方法对这些盲点无法有效发挥作用。在这种情况下，使用V2V通信是在各个车辆超出其检测范围之外去共享行人预警信息最好的方法之一。根据那些及时的协同预警信息，驾驶员可以做出更可靠的决策，并有更好的应对紧急情况的机会。但是，在行人协同预警过程中，有几个挑战。首先是位置信息的中断，即全球定位系统（GPS）接收机定位的位置与实际位置之间的差距。不准确的传感器信息导致不确定的车辆或行人状态信息，这会影响协同预警系统的性能。另一个挑战是冗余的信息，这意味着可能会使驱动程序承载过多的预警信息。为了减少行人碰撞概率，我们提出了一个新的协作预警框架，在V2V协作环境中共享行人预警信息。检测到的行人预警信息（包括行人的位置、速度和移动）可以在相关车辆的盲点中共享。上述的位置中断和冗余信息是该框架试图要解决的两个重要问题。为了降低位置中断带来的影响，该框架开发了一种采用载波相位差分技术的双GPS定位系统，一方面提高定位精度，另一方面对广播间隔进行优化27。更多细节将在第5节中讨论。在成功的预警与风险干扰的驱动程序之间提出一种权衡机制来释放冗余信息。在不失一般性的情况下，我们假设车辆行驶路线信息是已知的。因此，权衡问题可以转化为集群优化问题。后台信息处理中心（BIPC）首先根据车辆GPS