Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 153-157.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Realization of “Uncontrolled” Object Recognition Algorithm Based on Mobile Terminal

PANG Yu1, LIU Ping2, LEI Yin-jie1   

  1. College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China1;
    Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province,Chengdu 610065,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: Aiming at the problems that the existing object recognition methods are easy to be influenced by “uncontrolled” factors such as illumination,angle,size and complex environment,and have the problems such as low recognition rate,poor real-time performance and large memory consumption,this paper proposed a new object recognition algorithm,on which the object recognition system based on mobile terminal was realized.This method first employs particle filter algorithm to track the detection range by adding windows,and then applies the watershed segmentation algorithm to segment objects,then uses the HOG(Histogram of Oriented Gradient) algorithm to extract object features.Finally,the random forest algorithm is utilized to recognize objects.The experimental results show that this method can be used to identify the mobile terminal quickly and accurately in an “uncontrolled” environment.

Key words: Mobile terminal, Uncontrolled, Real time, Object recognition, Random forest

CLC Number: 

  • TP391.41
[1] SHAH S A A,BENNAMOUN M,BOUSSAID F.Automatic object detection using objectness measure[C]∥International Conference on Communications,Signal Processing,and Their Applications.IEEE,2013:1-6.
[2] 徐晓.计算机视觉中物体识别综述[J].电脑与信息技术,2013,21(5):4-6.
[3] 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,2014,36(6):1225-1240.
[4] 林志强,陈小平.一种结合多特征的实时物体识别系统[J].小型微型计算机系统,2015,36(6):1310-1315.
[5] 刘曦,史忠植,石志伟,等.一种基于特征捆绑计算模型的物体识别方法[J].软件学报,2010,21(3):452-460.
[6] 卢良锋,谢志军,叶宏武.基于RGB特征与深度特征融合的物体识别算法[J].计算机工程,2016,42(5):186-193.
[7] 孙利娟,张继栋,杨新锋.基于多稀疏分布特征和最近邻分类的物体识别方法[J].计算机应用研究,2016,33(10):3156-3159.
[8] 尚俊.基于HOG特征的目标识别算法研究[D].武汉:华中科技大学,2012.
[9] 唐发明,王仲东,陈绵云.一种新的二叉树多类支持向量机算法[J].计算机工程与应用,2005,41(7):24-26.
[10] 苏亚麟,吕开云.基于随机森林算法的特征选择的水稻分类——以南昌市为例[J]江西科学,2018(1):161-167.
[11] 周雪晴,张占松,张超谟,等.基于粗糙集—随机森林算法的复杂岩性识别[J].大庆石油地质与开发,2017,36(6):127-133.
[12] 闫月影.非受控场景下的二维人脸识别研究[J].数码世界,2017(11):394-395.
[13] 李安平.复杂环境下的视频目标跟踪算法研究[D].上海:上海交通大学,2007.
[14] 陈代武.基于移动终端的多角度实物识别方法[D].北京:北京邮电大学,2015.
[15] FRIEDMAN N,RUSSELL S.Image Segmentation in Video Sequences:A Probabilistic Approach,Uncertainty in Artificial Intelligence[J].arXiv:1302.1539,1997.
[16] HALEVY G,WEINSHALL D.Motion of Disturbances:Detection and Tracking of Multi-Body Non-Rigid Motion.Machine Vision and Applications,1999,11(3):122-137.
[17] KUMAR S,DAI Y,LI H.Spatio-Temporal Union of subspaces for Multi-body Non-rigid Structure-from-Motion[J].Pattern Recognition,2017,71:428-443.
[18] 曹正凤.随机森林算法优化研究[D].北京:首都经济贸易大学,2014.
[19] 许保勋.面向高维数据的随机森林算法优化研究[D].哈尔滨:哈尔滨工业大学,2013.
[20] 程广涛,陈雪,郭照庄.基于HOG特征的行为人视觉检测方法[J].传感器与微系统,2011,30(7):68-70.
[21] 赵桂儒.较大规模数据应用PCA降维的一种方法[J].电脑知识与技术,2014(8):1835-1837.
[22] 杨彪,倪蓉蓉,江大鹏.一种对光照变化鲁棒的移动目标前景提取方法[J].计算机科学,2016,43(s2):186-189.
[23] 郭宇,郝晓燕,张兴忠.基于预测的多特征融合Mean-Shift跟踪算法[J].计算机科学,2018,45(s1):171-173.
[1] LIU Zhen-peng, SU Nan, QIN Yi-wen, LU Jia-huan, LI Xiao-fei. FS-CRF:Outlier Detection Model Based on Feature Segmentation and Cascaded Random Forest [J]. Computer Science, 2020, 47(8): 185-188.
[2] YANG Wei-chao, GUO Yuan-bo, LI Tao, ZHU Ben-quan. Method Based on Traffic Fingerprint for IoT Device Identification and IoT Security Model [J]. Computer Science, 2020, 47(7): 299-306.
[3] ZHANG Yuan-ming, LI Meng-ni, HUANG Lang-you, LU Jia-wei, XIAO Gang. Data Composition View Positioning Update Approach with Incremental Logs [J]. Computer Science, 2020, 47(6): 85-91.
[4] LIU Yu-hong,LIU Shu-ying,FU Fu-xiang. Optimization of Compressed Sensing Reconstruction Algorithms Based on Convolutional Neural Network [J]. Computer Science, 2020, 47(3): 143-148.
[5] ZHAO Rui-jie, SHI Yong, ZHANG Han, LONG Jun, XUE Zhi. Webshell File Detection Method Based on TF-IDF [J]. Computer Science, 2020, 47(11A): 363-367.
[6] WANG Xiao-hui, ZHANG Liang, LI Jun-qing, SUN Yu-cui, TIAN Jie, HAN Rui-yi. Study on XGBoost Improved Method Based on Genetic Algorithm and Random Forest [J]. Computer Science, 2020, 47(11A): 454-458.
[7] ZENG Lei, LI Hao, LIN Yu-fei, ZHANG Shuai. Study on Simulation Optimization of Gazebo Based on Asynchronous Mechanism [J]. Computer Science, 2020, 47(11A): 593-598.
[8] ZHANG Zhou, HUANG Guo-rui, JIN Pei-quan. Task Scheduling on Storm:Current Situations and Research Prospects [J]. Computer Science, 2019, 46(9): 28-35.
[9] ZHANG Bin-bin, WANG Juan, YUE Kun, WU Hao, HAO Jia. Performance Prediction and Configuration Optimization of Virtual Machines Based on Random Forest [J]. Computer Science, 2019, 46(9): 85-92.
[10] ZHU De-li, YANG De-gang, HU Rong, WAN Hui. Adaptive Multi-level Threshold Binaryzation Method for Optical Character Recognition in Mobile Environment [J]. Computer Science, 2019, 46(8): 315-320.
[11] WU Gang,XU Li-min. Review of Shape Representation for Objects [J]. Computer Science, 2019, 46(7): 30-37.
[12] SHI Yu-xin, DENG Hong-min, GUO Wei-lin. Static Gesture Recognition Based on Hybrid Convolution Neural Network [J]. Computer Science, 2019, 46(6A): 165-168.
[13] CHEN Xi, LI Lei-da, LI Qiao-yue, HAN Xi-xi, ZHU Han-cheng. No-reference Quality Assessment of Depth Images Based on Natural Scenes Statistics [J]. Computer Science, 2019, 46(6): 256-262.
[14] CUI Jing-chun, WANG Jing. Face Expression Recognition Model Based on Enhanced Head Pose Estimation [J]. Computer Science, 2019, 46(6): 322-327.
[15] LI Zhi, MA Chun-lai, MA Tao, SHAN Hong. Anomaly Detection Method of Mobile Terminal User Based on Location Information [J]. Computer Science, 2019, 46(3): 180-187.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .