基于深度卷积神经网络的运动目标光流检测方法

王正来; 黄敏; 朱启兵; 蒋胜

doi:doi:10.12086/oee.2018.180027

光电工程, 2018, 45 (8): 180027, 网络出版: 2018-08-25

基于深度卷积神经网络的运动目标光流检测方法

The optical flow detection method of moving target using deep convolution neural network

论文大纲

王正来 ^*黄敏朱启兵蒋胜

作者单位

江南大学轻工过程先进控制教育部重点实验室，江苏无锡 214122

运动目标光流检测深度卷积神经网络网络结构优化 moving target optical flow detection deep convolutional neural network network structure optimization

摘要

运动目标检测是物体检测领域的一个重要研究方向，在目标识别中有着至关重要的作用。针对传统运动检测方法精度不高、无法对运动目标进行检测，本文将深度卷积神经网络引入到运动目标光流检测中，将前后帧图像及目标光流场图像作为网络的输入，自适应地学习运动目标光流，并通过对网络放大架构的优化及网络的精简，同时采用数据增广等技术，设计出精度与实时性兼顾的目标物体光流检测网络。实验结果表明，本文方法在运动目标的光流场检测中有更好的表现，SS-sp 和CS-sp 网络相比原网络在检测精度上均提高了约5.0%，同时大幅减少了网络的运行时间，基本满足实时检测的要求。

Abstract

Moving target detection is an important research direction of object detection, and it plays an important role in target recognition. The accuracy of traditional motion detection methods is low, which are unable to only detect the required moving target. In this study, deep convolutional neural network is introduced into the optical flow detection of moving target. In this method, a pair of images and optical flow fields of target are used as inputs of the network to adaptively study the target optical flow. Furthermore, through optimization of the expanding part of the network and the simplification of the network, and combined with many data augmentation technologies, the optical flow detection network of target object with both accuracy and real-time is designed. Experimental results show that the proposed method has better performance in the optical flow detection of moving target. SS-sp and CS-sp network are improved by about 5.0% compared to the original network on the precision and the runtime of the network is significantly reduced, which meet the requirements of real-time detection.

参考文献

[1] 黄凯奇, 任伟强, 谭铁牛. 图像物体分类与检测算法综述[J]. 计算机学报, 2014, 37(6): 1225–1240.

Huang K Q, Ren W Q, Tan T N. A review on image object classification and detection[J]. Chinese Journal of Computers, 2014, 37(6): 1225–1240.

[2] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理, 2016, 31(1): 1–17.

Lu H T, Zhang Q C. Applications of deep convolutional neural network in computer vision[J]. Journal of Data Acquisition and Processing, 2016, 31(1): 1–17.

[3] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1137.

[4] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431–3440.

[5] 王顺飞, 闫钧华, 王志刚. 改进的基于局部联合特征的运动目标检测方法[J]. 仪器仪表学报, 2015, 36(10): 2241–2248.

Wang S F, Yan J H, Wang Z G. Improved moving object detection algorithm based on local united feature[J]. Chinese Journal of Scientific Instrument, 2015, 36(10): 2241–2248.

[6] 刘威, 赵文杰, 李成, 等. 基于改进ORB 特征匹配的运动小目标检测[J]. 光电工程, 2015, 42(10): 13–20.

Liu W, Zhao W J, Li C, et al. Detecting small moving target based on the improved ORB feature matching[J]. Opto- Electronic Engineering, 2015, 42(10): 13–20.

[7] 黄超群. 基于混合高斯模型和Kalman 滤波器的运动目标检测与跟踪[D]. 昆明: 云南大学, 2010.

Huang C Q. Motion detection and tracking based on gaussian mixture model and kalman filter[D]. Kunming: Yunnan University, 2010.

[8] 张金敏, 王斌. 光照快速变化条件下的运动目标检测[J]. 光电工程, 2016, 43(2): 14–21.

Zhang J M, Wang B. Moving object detection under condition of fast illumination change[J]. Opto-Electronic Engineering, 2016, 43(2): 14–21.

[9] 袁国武, 陈志强, 龚健, 等. 一种结合光流法与三帧差分法的运动目标检测算法[J]. 小型微型计算机系统, 2013, 34(3): 668–671.

Yuan G W, Chen Z Q, Gong J, et al. A moving object detection algorithm based on a combination of optical flow and three-frame difference[J]. Journal of Chinese Computer Systems, 2013, 34(3): 668–671.

[10] 罗胜, Jiang Y Z. 视频检测烟雾的研究现状[J]. 中国图象图形学报, 2013, 18(10): 1225–1236.

Luo S, Jiang Y Z. State-of-art of video based smoke detection algorithms[J]. Journal of Image and Graphics, 2013, 18(10): 1225–1236.

[11] Shi L F, Long F, Zhan Y J, et al. Video-based fire detection with spatio-temporal SURF and color features[C]//Proceedings of 2016 12th World Congress on Intelligent Control and Automation, 2016: 258–262.

[12] Dosovitskiy A, Fischery P, Ilg E, et al. Flownet: learning optical flow with convolutional networks[C]// Proceeding of 2015 IEEE International Conference on Computer Vision, 2015: 2758–2766.

[13] Ilg E, Mayer N, Saikia T, et al. Flownet 2.0: evolution of optical flow estimation with deep networks[C]//Proceeding of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1647–1655.

[14] 邓昊. 神经元图像识别相关方法探究[D]. 武汉: 湖北大学, 2016.

Deng H. Research on neuron image recognition[D]. Wuhan: Hubei University, 2016.

[15] Ouyang P, Hu H, Shi Z Z. Plankton classification with deep convolutional neural networks[C]//Proceeding of 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, 2016: 132–136.

[16] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceeding of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.

[17] He K M, Zhang X Y, Ren S Q, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[ C]//Proceeding of 2015 IEEE International Conference on Computer Vision, 2015: 1026–1034.

[18] He K M, Sun J. Convolutional neural networks at constrained time cost[C]//Proceeding of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3992–4000.

[19] Horn B K P, Schunck B G. Determining optical flow[J]. Artificial Intelligence, 1981, 17(1–3): 185–203.

[20] Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision (DARPA)[C]//Proceedings of the 1981 DARPA Image Understanding Workshop, 1981: 121–130.

王正来, 黄敏, 朱启兵, 蒋胜. 基于深度卷积神经网络的运动目标光流检测方法[J]. 光电工程, 2018, 45(8): 180027. Wang Zhenglai, Huang Min, Zhu Qibing, Jiang Sheng. The optical flow detection method of moving target using deep convolution neural network[J]. Opto-Electronic Engineering, 2018, 45(8): 180027.

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