Abstract
We integrate human detection and regional affine invariant feature tracking into a robust human tracking system. First, foreground blobs are detected using background subtraction. The background model is built with a local predictive model to cope with large illumination changes. Detected foreground blobs are then used by a box tracker to establish stable tracks of moving objects. Human detection hypotheses are detected using a combination of both shape and region information through a hierarchical part-template matching method. Human detection results are then used to refine tracks for moving people. Track refinement, extension and merging are carried out with a robust tracker that is based on regional affine invariant features. We show experimental results for the separate components as well as the entire system.
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References
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking, pp. 245–252. IEEE Computer Society, Washington, DC, USA (1999)
Kilger, M.: A shadow handler in a video-based real-time traffic monitoring system. In: Proc. IEEE Workshop Applications of Computer Vision, pp. 11–18. IEEE Computer Society, Los Alamitos (1992)
Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.S.: Background modeling and subtraction by codebook construction, pp. 3061–3064. IEEE Computer Society, Washington (2004)
Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning, pp. 189–196. Springer, London (1994)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: ICCV (2005)
Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)
Gavrila, D.M., Philomin, V.: Real-time object detection for smart vehicles. In: ICCV (1999)
Zhao, T., Nevatia, R.: Mcmc-based approach for human segmentation. In: CVPR (2004)
Smith, K., Perez, D.G., Odobez, J.M.: Using particles to track varying numbers of interacting people. In: CVPR (2005)
Wang, H., Suter, D., Schindler, K.: Effective appearance model and similarity measure for particle filtering and visual tracking. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 606–618. Springer, Heidelberg (2006)
Han, B., Davis, L.: On-line density-based appearance modeling for object tracking. In: Proc. IEEE ICCV 2005, pp. 1492–1499. IEEE Computer Society, Los Alamitos (2005)
Jepson, A., Fleet, D., El-Maraghi, T.: Robust online appearance models for visual tracking. IEEE Trans. PAMI 25(10) (2003)
Matas, J., Chum, O., Martin, U., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proc. BMVC 2002 London, pp. 384–393 (2002)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int’l J. Computer Vision 65(1-2), 43–72 (2005)
Collins, R., Liu, Y., Leordeanu, M.: On-line selection of discriminative tracking features. IEEE Trans. PAMI 27(10), 1631–1643 (2005)
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Tran, S., Lin, Z., Harwood, D., Davis, L. (2008). UMD_VDT, an Integration of Detection and Tracking Methods for Multiple Human Tracking. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_15
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DOI: https://doi.org/10.1007/978-3-540-68585-2_15
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