Abstract
This paper proposes a novel vision based multi-pedestrian tracking scheme in crowded scenes, which are very common in real-world applications. The major challenge of the multi-pedestrian tracking problem comes from complicated occlusions, cluttered or even changing background. We address these issues by creatively combining state-of-the-art pedestrian detectors and clustering algorithms. The core idea of our method lies in the integration of local information provided by pedestrian detector and global evidence produced by cluster analysis. A prediction algorithm is proposed to return the possible locations of missed target in offline detection, which will be re-detected by online detectors. The pedestrian detector in use is an online adaptive detector mainly based on texture features, which can be replaced by more advanced ones if necessary. The effectiveness of the proposed tracking scheme is validated on a real-world scenario and shows satisfactory performance.
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References
Zhao, T., Nevatia, R.: Bayesian Human Segmentation in Crowded Situations. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 459–466 (2003)
Rittscher, J., Tu, P.H., Krahnstoever, N.: Simultaneous Estimation of Segmentation and Shape. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 486–493 (2005)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian Detection in Crowded Scenes. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 878–885 (2005)
Chang, C., Ansari, R., Khokhar, A.: Multiple Objects Tracking with Kernel Particle Filter. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 566–573 (2005)
Ge, W., Collins, R.T.: Multi-Target Data Association by Tracklets with Unsupervised Parameter Estimation. In: 19th British Machine Vision Conference, pp. 93.1–93.10 (2008)
Shafique, K., Shah, M.: A Noniterative Greedy Algorithm for Multiframe Point Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1), 51–65 (2005)
Jiang, H., Fels, F., Little, J.J.: A Linear Programming Approach for Multiple Object Tracking. In: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Sidla, O., Lypetskyy, Y., Brandle, N., Seer, S.: Pedestrian Detection and Tracking for Counting Applications in Crowded Situations. In: 2006 IEEE International Conference on Video and Signal Based Surveillance, p. 70 (2006)
Lin, S.F., Chen, J.Y., Chao, H.X.: Estimation of Number of People in Crowded Scenes Using Perspective Transformation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 31(6), 645–654 (2001)
Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera People Tracking with A Probabilistic Occupancy Map. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 267–282 (2008)
Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple Object Tracking Using K-shortest Paths Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)
Zhang, L., Li, Y., Nevatia, R.: Global Data Association for Multi-object Tracking Using Network flows. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Kalal, Z., Mikolajczyk, K., Matas, J.: Face-tld: Tracking-Learning-Detection Applied to Faces. In: 2010 IEEE International Conference on Image Processing, pp. 3789–3792 (2010)
Wu, B., Nevatia, R.: Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet Based Part Detectors. International Journal of Computer Vision 75(2), 247–266 (2007)
Pereira, S., Pun, T.: Robust Template Matching for Affine Resistant Image Watermarks. In: 2000 IEEE International Conference on Image Processing, pp. 1123–1129 (2000)
CAVIAR Dataset, http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1
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Yang, Z., Yuan, B. (2013). Vision Based Multi-pedestrian Tracking Using Adaptive Detection and Clustering. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_8
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DOI: https://doi.org/10.1007/978-3-642-41278-3_8
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