Abstract
Tracking object accurately in one frame per minute (1-fpm) video is believed to be impossible, because the one-minute discontinuity of object coupled with dynamic background variation implies that the motion and appearance of target is theoretically not predictable. In the context of maritime boat ramps traffic surveillance, we propose in this paper a novel approach to tracking object in the low-frame-rate (LFR) of 1-fpm videos, where the motion discontinuity of object is mitigated by adopting target lifespan path-template and association rules of behavior prediction. The approach has been applied to trailer boat counting at three maritime boat ramps in New Zealand. The obtained accuracy goes above 90 %, with reference to the ground truth manual counting.
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References
Rui, Y., Chen, Y.: Better proposal distributions: Objects tracking using unscented particle filter. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 786–793 (2001)
Zhou, S., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Process 13(11), 1491–1506 (2004)
Bruno, M.: Bayesian methods for multiaspect target tracking in image sequences. IEEE Trans. Sig. Process 52(7), 1848–1861 (2004)
Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1820–1833 (2010)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of nonrigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 142–149 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Wang, L., Yan, H., Wu, H., Pan, C.: Forward-backward mean-shift for visual tracking with local-background-weighted histogram. IEEE Trans. Intell. Transp. Syst. 14(3), 1480–1489 (2013)
Shen, C., Kim, J., Wang, H.: Generalized kernel-based visual tracking. IEEE Trans. Circuits Syst. Video Technol. 20(1), 119–130 (2010)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 13–58 (2006). p. article 13
Porikli, F., Tuzel, O.: Object tracking in low-frame-rate video. In: Image and Video Communications and Processing 2005, pp. 72–79 (2005)
Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1728–1740 (2008)
Chuang, M., Hwang, J., Williams, K., Towler, R.: Tracking live fish from low-contrast and low-frame-rate stereo videos. IEEE Trans. Circuits Syst. Video Technol. 25(1), 167–179 (2015)
Zhao, J., Pang, S., Hartill, B., Sarrafzadeh, A.H.: Adaptive background modeling for land and water composition scenes. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9280, pp. 97–107. Springer, Heidelberg (2015)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)
Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)
Khan, Z., Balch, T., Dellaert, F.: Mcmc-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Machine Intell. 27(11), 1805–1918 (2005)
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Zhao, J., Pang, S., Hartill, B., Sarrafzadeh, A. (2016). Object Trajectory Association Rules for Tracking Trailer Boat in Low-frame-rate Videos. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_38
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DOI: https://doi.org/10.1007/978-3-319-40663-3_38
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