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.
The optical flow detection method of moving target using deep convolution neural network
First published at:Aug 01, 2018
1 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 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. DOI:10.1109/TPAMI.2016.2577031
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 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. DOI:10.3969/j.issn.0254-3087.2015.10.011
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. DOI:10.3969/j.issn.0254-3087.2015.10.011
6 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. DOI:10.3969/j.issn.1003-501X.2015.10.003
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. DOI:10.3969/j.issn.1003-501X.2015.10.003
7 Huang C Q. Motion detection and tracking based on gaussian mixture model and kalman filter[D]. Kunming: Yunnan University, 2010.
8 Zhang J M, Wang B. Moving object detection under condition of fast illumination change[J]. Opto-Electronic Engineering, 2016, 43(2): 14-21. DOI:10.3969/j.issn.1003-501X.2016.02.003
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. DOI:10.3969/j.issn.1003-501X.2016.02.003
9 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 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. DOI:10.11834/jig.20131002
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. DOI:10.11834/jig.20131002
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.
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. DOI:10.1016/0004-3702(81)90024-2
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.
the Research Innovation Program for College Graduates of Jiangsu Province (SJLX16_0498) and the Prospective Joint Research Foundation of Jiangsu Province of China (BY2016022-32)
Get Citation: Wang Zhenglai, Huang Min, Zhu Qibing, et al. The optical flow detection method of moving target using deep convolution neural network[J]. Opto-Electronic Engineering, 2018, 45(8): 180027.
Previous: Algorithm for object detection and tracking combined on four inter-frame difference and optical flow methods