1. 毕业设计（论文）主要内容：

构建基于移动手机端的微信小程序和基于微服务的云平台，通过手机获取交通道路上的常见图像，并在手机端进行预处理。云端用于离线训练模型，在线检测图像中的人，车辆，自行车，路标牌，广告牌等目标，最终将检测出的目标信息返回给微信小程序展示。

1） 1) 采用的技术:

云平台的搭建可以采用kubernetes的云平台设计与管理技术，kubernetes是google开源的容器集群管理系统，其提供应用部署、维护、扩展机制等功能，利用kubernetes能方便地管理跨机器运行容器化的应用。

2. 毕业设计（论文）主要任务及要求

1. 查阅15篇相关文献（含2篇外文），并每篇书写200—300字文献摘要（装订成册，带封面）；

2. 认真填写周记，完成800字开题报告；

3. 完成5000中文字以上的相关英文专业文献翻译，并装订成册（中英文一起，带封面）；

3. 毕业设计（论文）完成任务的计划与安排

(1) 2018/1/14—2018/3/5：确定选题，查阅文献，外文翻译和撰写开题报告；

(2) 2018/3/6—2018/4/30：系统架构、程序设计与开发、系统测试与完善；

(3) 2018/5/1—2018/5/25：撰写及修改毕业论文；

4. 主要参考文献

[1] x. zhao, h. ma, h. zhang, y. tang, et al., “hvpi: extending hadoop to support video analytic applications,” ieee international conference on cloud computing , pp. 789-796, 2015
[2] x. liu, w. liu, h. ma, et al., “large-scale vehicle re-identification in urban surveillance videos,” ieee international conference on multimedia and expo (icme), pp. 1-6, 2016.
[3] j. redmon, s. divvala, r. girshick, and a. farhadi, “you only look once: unified, real-time object detection,” 2016 ieee conference on computer vision and pattern recognition (cvpr), pp. 779-788, 2016.
[4] h. viswanathan, e. k. lee, i. rodero, et al., “uncertainty-aware autonomic resource provisioning for mobile cloud computing,” ieee transactions on parallel and distributed systems, pp. 2363-2372, 2015.
[5] k. kang, w. ouyang, h. li, et al., “object detection from video tubelets with convolutional neural networks,” 2016 ieee conference on computer vision and pattern recognition (cvpr), pp. 817-825, 2016.
[6] s. he and rynson w.h. lau, “oriented object proposals,” 2015 ieee international conference on computer vision (iccv), pp. 280-288, 2015
[7] c. wang, l. zhao, s. liang, et al., “object proposal by multi-branch hierarchical segmentation,” ieee conference on computer vision and pattern recognition (cvpr), pp. 3873-3881, 2015.
[8] h. liu and b. he, “vmbuddies: coordinating live migration of multi-tier applications in cloud environments,” ieee transactions on parallel and distributed systems, pp. 1192-1205, 2015
[9] t. kong, a. yao, y. chen, et al., “hypernet: towards accurate region proposal generation and joint object detection,” 2016 ieee conference on computer vision and pattern recognition (cvpr), pp. 845-853, 2016
[10] s. gidaris and n. komodakis, “locnet: improving localization accuracy for object detection, ” 2016 ieee conference on computer vision and pattern recognition (cvpr), pp. 789-798, 2016
[11] d. muramatsu, a. shiraishi, y. makihara, et al., “gait-based person recognition using arbitrary view transformation model,” ieee transactions on image processing, pp. 140-154, 2015
[12] j. li, c. xia, x. chen, “a benchmark dataset and saliency-guided stacked autoencoders for video-based salient object detection,” ieee transactions on image processing, pp. 349-364, 2018
[13] x. lu, y. chen, x.li, “hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features,” ieee transactions on image processing, pp. 106-120, 2018
[14] n. liu, z. li, j. xu, et al., “a hierarchical framework of cloud resource allocation and power management using deep reinforcement learning,” 2017 ieee 37th international conference on distributed computing systems (icdcs), pp. 372-382, 2017
[15] s. yang, p. luo, c. c. loy and x. tang, "from facial parts responses to face detection: a deep learning approach," 2015 ieee international conference on computer vision (iccv), santiago, 2015, pp. 3676-3684.