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基于增強(qiáng)群跟蹤器和深度學(xué)習(xí)的目標(biāo)跟蹤

程帥 曹永剛 孫俊喜 趙立榮 劉廣文 韓廣良

程帥, 曹永剛, 孫俊喜, 趙立榮, 劉廣文, 韓廣良. 基于增強(qiáng)群跟蹤器和深度學(xué)習(xí)的目標(biāo)跟蹤[J]. 電子與信息學(xué)報(bào), 2015, 37(7): 1646-1653. doi: 10.11999/JEIT141362
引用本文: 程帥, 曹永剛, 孫俊喜, 趙立榮, 劉廣文, 韓廣良. 基于增強(qiáng)群跟蹤器和深度學(xué)習(xí)的目標(biāo)跟蹤[J]. 電子與信息學(xué)報(bào), 2015, 37(7): 1646-1653. doi: 10.11999/JEIT141362
Cheng Shuai, Cao Yong-gang, Sun Jun-xi, Zhao Li-rong, Liu Guang-wen, Han Guang-liang. Target Tracking Based on Enhanced Flock of Tracker and Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1646-1653. doi: 10.11999/JEIT141362
Citation: Cheng Shuai, Cao Yong-gang, Sun Jun-xi, Zhao Li-rong, Liu Guang-wen, Han Guang-liang. Target Tracking Based on Enhanced Flock of Tracker and Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1646-1653. doi: 10.11999/JEIT141362

基于增強(qiáng)群跟蹤器和深度學(xué)習(xí)的目標(biāo)跟蹤

doi: 10.11999/JEIT141362
基金項(xiàng)目: 

國家自然科學(xué)基金(61172111)和吉林省科技廳項(xiàng)目(20090512, 20100312)資助課題

Target Tracking Based on Enhanced Flock of Tracker and Deep Learning

  • 摘要: 為解決基于外觀模型和傳統(tǒng)機(jī)器學(xué)習(xí)目標(biāo)跟蹤易出現(xiàn)目標(biāo)漂移甚至跟蹤失敗的問題,該文提出以跟蹤-學(xué)習(xí)-檢測(cè)(TLD)算法為框架,基于增強(qiáng)群跟蹤器(FoT)和深度學(xué)習(xí)的目標(biāo)跟蹤算法。FoT實(shí)現(xiàn)目標(biāo)的預(yù)測(cè)與跟蹤,增添基于時(shí)空上下文級(jí)聯(lián)預(yù)測(cè)器提高預(yù)測(cè)局部跟蹤器的成功率,快速隨機(jī)采樣一致性算法評(píng)估全局運(yùn)動(dòng)模型,提高目標(biāo)跟蹤的精確度。深度去噪自編碼器和支持向量機(jī)分類器構(gòu)建深度檢測(cè)器,結(jié)合全局多尺度掃描窗口搜索策略檢測(cè)可能的目標(biāo)。加權(quán)P-N學(xué)習(xí)對(duì)樣本加權(quán)處理,提高分類器的分類精確度。與其它跟蹤算法相比較,在復(fù)雜環(huán)境下,不同圖片序列實(shí)驗(yàn)結(jié)果表明,該算法在遮擋、相似背景等條件下具有更高的準(zhǔn)確度和魯棒性。
  • Wu Y, Lim J, and Yang M H. Online object tracking: A benchmark[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411-2418.
    Ross D A, Lim J, Lin R S, et al.. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(3): 125-141.
    Babenko B, Yang M H, and Belongie S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
    陳東成, 朱明, 高文, 等. 在線加權(quán)多示例學(xué)習(xí)實(shí)時(shí)目標(biāo)跟蹤[J]. 光學(xué)精密工程, 2014, 22(6): 1661-1667.
    Chen Dong-cheng, Zhu Ming, Gao Wen, et al.. Real-time object tracking via online weighted multiple instance learning [J]. Optics and Precision Engineerin, 2014, 22(6): 1661-1667.
    He S F, Yang Q X, Rynson L, et al.. Visual Tracking via Locality Sensitive Histograms[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2427-2434.
    Grabner H, Grabner M, and Bischof H. Real-time tracking via online boosting[C]. Proceedings of British Machine Vision Conference, Edinburgh, UK, 2006: 47-56.
    Grabner H, Leistner C, and Bischof H. Semi-supervised on-line boosting for robust tracking[C]. Proceedings of European Conference on Computer Vision, Berlin, Germany, 2008: 234-247.
    顏佳, 吳敏淵. 遮擋環(huán)境下采用在線Boosting的目標(biāo)跟蹤[J]. 光學(xué)精密工程, 2012, 20(2): 439-446.
    Yan Jia and Wu Ming-yuan. On-line boosting based target tracking under occlusion[J]. Optics and Precision Engineering, 2012, 20(2): 439-446.
    Kalal Z, Matas J, and Mikolajczyk K. P-N learning: bootstrapping binary classifiers by structural constraints[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, 2010: 49-56.
    鄭胤, 陳權(quán)崎, 章毓晉. 深度學(xué)習(xí)及其在目標(biāo)和行為識(shí)別中的新進(jìn)展[J]. 中國圖像圖形學(xué)報(bào), 2014, 19(2): 175-184.
    Zheng Ying, Chen Quan-qi, and Zhang Yu-jin. Deep learning and its new progress in object and behavior recognition[J]. Journal of Image and Graphic, 2014, 19(2): 175-184.
    Tomas V and Jiri M. Robustifying the flock of trackers[C]. Proceedings of Computer Vision Winter Workshop, Graz, Austria, 2011: 91-97.
    周鑫, 錢秋朦, 葉永強(qiáng), 等. 改進(jìn)后的TLD視頻目標(biāo)跟蹤方法[J]. 中國圖象圖形學(xué)報(bào), 2013, 18(9): 1115-1123.
    Zhou Xin, Qian Qiu-meng, Ye Yong-qiang, et al.. Improved TLD visual target tracking algorithm[J]. Journal of Image and Graphic, 2013, 18(9): 1115-1123.
    Kalal Z, Mikolajczyk K, and Matas J. Tracking-learning- detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
    Zhang K, Zhang L, Liu Q, et al.. Fast visual tracking via dense spatio-temporal context learning[C]. Proceedings of European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141.
    Botterill T, Mills S, and Green R D. New conditional sampling strategies for speeded-up RANSAC[C]. Proceedings of British Machine Vision Conference, London, UK, 2009: 1-11.
    Vincent P, Larochelle H, Lajoie I, et al.. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(2): 3371-3408.
    Tang Yi-chuan. Deep learning using linear support vector machines[C]. Proceedings of International Conference on Machine Learning: Challenges in Representational Learning Workshop, Atlanta, USA, 2013: 266-272.
    Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
    Torralba A, Fergus R, and Freeman W T. 80 million tiny images: a large data set for nonparametric object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1958-1970.
    高文, 湯洋, 朱明. 復(fù)雜背景下目標(biāo)檢測(cè)的級(jí)聯(lián)分類器算法研究[J]. 物理學(xué)報(bào), 2014, 63(9): 094204.
    Gao Wen, Tang Yang, and Zhu Ming. Study on the cascade classifier in target detection under complex background[J]. Acta Physica Sinica, 2014, 63(9): 094204.
    Collins R T, Zhou X H, and Teh S K. An open source tracking test bed and evaluation web site[C]. Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Breckenridge, USA, 2005: 17-24.
    Stalder S, Grabner H, and Van G L. Beyond semi-supervised tracking: tracking should be as simple as detection, but not simpler than recognition[C]. Proceedings of IEEE Conference on Computer Vision Workshops, Kyoto, Japan, 2009: 1409-1416.
    Dinh T B, Vo N, and Medion G. Context tracker: exploring supporters and distracters in unconstrained environments[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2011: 1177-1184.
    Qian Yu, Thang B D, and Gerard M. Online tracking and reacquisition using co-trained generative and discriminative trackers[C]. Proceedings of European Conference on Computer Vision, Marseille, France, 2008: 678-691.
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出版歷程
  • 收稿日期:  2014-10-29
  • 修回日期:  2015-03-23
  • 刊出日期:  2015-07-19

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