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1、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
2、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
3、 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. F
4、irst, 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 warnin
5、g 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 result
6、ing 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
7、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
8、 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 pr
9、obability 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 pe
10、destrian 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
11、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. Probabili
12、ty-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 propose
13、d 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
14、 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
15、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
16、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 t
