Computer Science ›› 2016, Vol. 43 ›› Issue (11): 297-299, 312.doi: 10.11896/j.issn.1002-137X.2016.11.057

Previous Articles     Next Articles

Research of Video Abstraction Based on Object Detection and Tracking

TIAN Helei, DING Sheng, YU Changwei and ZHOU Li   

  • Online:2018-12-01 Published:2018-12-01

Abstract: ion Based on Object Detection and Tracking TIAN He-lei1 DING Sheng2 YU Chang-wei1 ZHOU Li2 (School of Instrument Science and Opto-electronics Engineering,Hefei University of Technology,Hefei 230009,China)1 (School of Computer and Information,Hefei University of Technology,Hefei 230009,China)2 Abstract In order to carry out a summary of massive surveillance video under the premise of not losing useful information,a video summarization technique based on object detection and tracking was proposed.Moving targets is detected by background subtraction in video by using Gaussian mixture model,and target tracking for detected target is done by using the idea of hierarchical correlation to get complete information of moving targets.Finally,the moving objects and video background are reassembled into the abstract video.The experimental results show that the method proposed in this paper can be used to concentrate the monitoring video.The summary of the video can completely preserve the original video,which reduces the storage space and cost.It is also convenient for relevant personnel to obtain useful information in time and improve work efficiency.

Key words: Video abstraction,Video condensation,Object detection,Object tracking,Background modeling,Hierarchical correlation

[1] Kumar N S,Shobha G,Balaji S.Key frame extraction algorithm for video abstraction applications in underwater videos[C]∥Underwater Technology(UT),2015.IEEE,2015:1-5
[2] Song G H,Ji Q G,Lu Z M,et al.A novel video abstraction method based on fast clustering of the regions of interest in key frames[J].AEU-International Journal of Electronics and Communications,2014,68(8):783-794
[3] Wang Ya-pei,Li Ren-wang,Liu Xiang.Extraction Method ofSurveillance Vudeo Synopsis Combines Objects and Keyframes[J].Industrial Control Computer,2015(3):11-13(in Chinese) 王亚沛,李仁旺,刘翔.对象和关键帧相结合的监控视频摘要提取方法[J].工业控制计算机,2015(3):11-13
[4] Zhang P,Zhuo T,Zhang Y,et al.Real-time Tracking-by-Lear-ning with High-order Regularization Fusion for Big Video Abstraction[J].Signal Processing,2015,124(c):246-258
[5] Liu Shou-da.Video Synopsis Based on Multi-target Trackingand Trajectory Combinatorial Optimization[D].Xiamen:Xiamen University,2014(in Chinese) 刘守达.基于多目标跟踪及轨迹组合优化的视频摘要[D].厦门:厦门大学,2014
[6] Han Jan-kang.Moving Area Detection and Tracking Based Vi-deo Condensation[D].Beijing:Beijing University of Posts and Telecommunications,2012(in Chinese) 韩建康.基于运动检测及跟踪的视频浓缩方法研究[D].北京:北京邮电大学,2012
[7] Felzenszwalb P,Girshick R,McAllester D,et al.Object dete-ction with discriminatively trained part based models[J].Pattern Analysis and Machine Intelligence(PAMI),2010,32(9):1627-1645
[8] Dalal N,Triggs B.Histograms of oriented gradients for human detection [C]∥Computer Vision and Pattern Recognition(CVPR).San Diego,CA,2005,1:886-893
[9] Wu B,Nevatia R.Detection and tracking of multiple,partially occluded humans by bayesian combination of edgelet based part detectors[J].International Journal of Computer Vision,2007,75(2):247-266
[10] Song Xue-hua,Chen Yu,Geng Jian-feng.Moving object detection based in improved Gaussian mixture background model [J].Computer Engineering and Design,2010,31(21):4646-4649(in Chinese) 宋雪桦,陈瑜,耿剑锋.基于改进的混合高斯背景模型的运动目标检测[J].计算机工程与设计,2010,1(21):4646-4649
[11] Zivkovic Z.Improved Adaptive Gaussian Mixture Model forBackground Subtraction[C]∥Proceedings of the International Conference on Recongnition Pattern,Amsterdam University.Netherlands,2004,2:23-26
[12] Huang C,Li Y,Nevatia R.Multiple target tracking by learning-based hierarchical association of detection responses[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(4):898-910
[13] Wang Jiang-feng.Researches on Object Tracking and Event Detection Based on Tracklet Association[D].Changsha: National University of Defense Technology,2011(in Chinese) 王江峰.基于轨迹片段关联的目标跟踪与事件检测方法研究[D].长沙:国防科学技术大学,2011
[14] Ristani E,Tomasi C.Tracking Multiple People Online and inReal Time[M]∥Computer Vision--ACCV 2014.Springer International Publishing,2015:444-459

No related articles found!
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, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] 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 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] 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 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] 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, 116 .