Please wait a minute...
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Real-time detection and discrimination of static objects and ghosts
YE Fang-fang1, 2, XU Li1
1.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;2.College of Electronic and Information Engineering, Jiangsu University, Zhenjiang 210023, China
Download:   PDF(1523KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A real-time detection method was proposed for static objects and ghosts detection and discrimination in order to solve the interference of ghost in video-based security surveillance. Two backgrounds were constructed to detect static foregrounds. Then an evidence aggregate image and a permitted occlusion time parameter were introduced in order to reduce  false alarms and handle occlusions. Canny edge operator was applied to extract the edge of the static foregrounds region, and boundary spatial color contrast in the background and current frame was computed. The static objects and ghosts were discriminated by comparing the boundary spatial color contrast. Experimental results over a heterogeneous dataset demonstrate that the approach can effectively detect stationary foregrounds from complex scenes and rapidly discriminate static objects and ghosts with a high accuracy, greatly reduce the computational cost of the discrimination task. The method runs average around 50 frames per second for a sequence with an image resolution of 352×288, which can be applied to real-time surveillance tasks.



Published: 06 June 2018
CLC:  TP 391  
Cite this article:

YE Fang-fang, XU Li. Real-time detection and discrimination of static objects and ghosts. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(1): 181-185.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2015.01.026     OR     http://www.zjujournals.com/eng/Y2015/V49/I1/181


实时的静止目标与鬼影检测及判别方法

针对视频安全监控中出现的鬼影干扰现象,提出静止目标与鬼影的实时检测及判别方法.建立双背景模型检测出静止前景区域,通过证据累积图像和允许遮挡时间参数减少虚警及处理遮挡问题.采用canny算子提取静止前景的边缘,分别计算静止前景边缘在当前帧与背景帧中的边界颜色对比度.通过对比边界颜色对比度的大小区分静止目标与鬼影.在不同复杂度的视频场景下的实验表明,采用该方法能够有效地从复杂场景中检测出静止目标,并快速判别鬼影,极大地降低了计算耗时且准确率较高.对于图像大小为 352×288 的视频序列,该算法的平均运行速度约为50 帧/s,能够满足实时的监控任务需求.

[1] HASSAN W, MITRA B, CHATWIN C, et al. Illumination invariant method to detect and track left luggage in public areas [C]∥ International Society for Optical Engineering. Brussels:SPIE,2010.
[2] FERRYMAN K, HOGG J, SOCHMAN D, et al. Robust abandoned object detection integrating wide area visual surveillance and social context [J]. Pattern Recognition Letters, 2013, 34 (7): 789-798 .
[3] PORIKLI F, IVANOV Y, HAGA T. Robust abandoned object detection using dual foregrounds [J]. EURASIP Journal on Advances in Signal Process, 2008(30): 110.
[4] XU Li-li, ZHANG Chao, ZHANG Duo. Abandoned objects detection using double illumination invariant foreground masks [C]∥International Conference on Pattern Recognition. Istanbul: IEEE, 2010: 436-439.
[5] TIAN Ying-li, FERIS RS, LIU Hao-wei, et al. Robust detection of abandoned and removed objects in complex surveillance videos [J]. IEEE Transactions on Systems, Man, and Cybernetics, 2011(9): 565-576.
[6] STAUFFER C, GRIMSON W E L. Adaptive background mixture models for real-time tracking [C]∥Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D C: IEEE, 1999: 245-252.
[7] CUUCHIARA R, GRANA C, PICCARDI M, et al. Detecting moving objects, ghosts, and shadows in video streams [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (10): 1337-1342.
[8] SPAGNOLO P, CAROPPO A, LEO M, et al.An abandoned / removed objects detection algorithm and its evaluation on PETS datasets [C]∥International Conference on Advanced Video and Signal Based Surveillance. Australia: IEEE, 2006: 17-21.
[9] VENEIANER P, ZHANG Z, YIN W, et al. Stationary target detection using the object video surveillance system [C]∥International Conference on Advanced Video and Signal Based Surveillance. London: IEEE, 2007(9): 242-247.
[10] FERRANDO S, GERA G, REGAZZONI C. Classification of unattended and stolen objects in video surveillance system [C]∥International Conference on Advanced Video and Signal Based Surveillance. Australia: IEEE, 2006: 21-27.
[11] LU S, ZHANG J, FENG D. Detecting ghost and left objects in video surveillance [J]. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23(7): 1503-1525.
[12] CARO L, SANMIGUEL J C, MARTINEZ J M. Discrimination of abandoned and stolen object based on active contours [C]∥International Conference on Advanced Video and Signal Based Surveillance. Klagenfurt: IEEE, 2011: 101-106.
[13] YANG Tao, LI Stan-zi, PAN Quan, et al. Real-time and accurate segmentation of moving objects in dynamic scene [C]∥Proceedings of ACM Multimedia-2nd International Workshop on Video Surveillance and Sensor Networks. New York: ACM, 2004: 136-143.
[14] ERDEM C E, SANKUR B, TEKALP M. Performance measures for video object segmentation and tracking [J]. IEEE Transactions on Image Process, 2004, 13(7):937-951.
[15] AVSS2007\[EB/OL\].\[2013-10-09\].http:∥www-vpu.eps.uam.es/ASODd.  

[1] HE Xue-jun, WANG Jin, LU Guo-dong, LIU Zhen-yu, CHEN Li, JIN Jing. 3D head portrait sculpture by industrial robot based on triangular mesh slicing and collision detection[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1104-1110.
[2] WANG Hua, HAN Tong-yang, ZHOU Ke. KeyGraph-based community detection algorithm for public security intelligence[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1173-1180.
[3] YOU Hai-hui, MA Zeng-yi, TANG Yi-jun, WANG Yue-lan, ZHENG Lin, YU Zhong, JI Cheng-jun. Soft measurement of heating value of burning municipal solid waste for circulating fluidized bed[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1163-1172.
[4] BI Xiao-jun, WANG Jia-hui. Teaching-learning-based optimization algorithm with hybrid learning strategy[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(5): 1024-1031.
[5] WANG Liang, YU Zhi-wen, GUO Bin. Moving trajectory prediction model based on double layer multi-granularity knowledge discovery[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 669-674.
[6] LIAO Miao, ZHAO Yu-qian, ZENG Ye-zhan, HUANG Zhong-chao, ZHANG Bing-kui, ZOU Bei-ji. Automatic segmentation for cell images based on support vector machine and ellipse fitting[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 722-728.
[7] HUANG Zheng-yu, JIANG Xin-long, LIU Jun-fa, CHEN Yi-qiang, GU Yang. Fusion feature based semi-supervised manifold localization method[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 655-662.
[8] JIANG Xin-long, CHEN Yi-qiang, LIU Jun-fa, HU Li-sha, SHEN Jian-fei. Wearable system to support proximity awareness for people with autism[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 637-647.
[9] MU Jing-jing, ZHAO Xin-yue, HE Zai-xing, ZHANG Shu-you. Contour reconstruction of overlapped bubbles based on concave-convex transformation and circle fitting[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 714-721.
[10] DAI Cai-yan, CHEN Ling, LI Bin, CHEN Bo-lun. Sampling-based link prediction in complex networks[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 554-561.
[11] LIU Lei, YANG Peng, LIU Zuo-jun. Locomotion-Mode recognition using multiple kernel relevance vector machine[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 562-571.
[12] GUO Meng-li, DA Fei-peng, DENG Xing, GAI Shao-yan. 3D face recognition based on keypoints and local feature[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 584-589.
[13] WANG Hai jun, GE Hong juan, ZHANG Sheng yan. Fast object tracking algorithm via kernel collaborative presentation[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 399-407.
[14] ZHANG Ya nan, CHEN De yun, WANG Ying jie, LIU Yu peng. Incremental graph pattern matching based dynamic recommendation method for cold-start user[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 408-415.
[15] LIU Yu peng, QIAO Xiu ming, ZHAO Shi lei, MA Chun guang. Deep combination of large-scale features in statistical machine translation[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(1): 46-56.