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Tracking by Detection Algorithms Using Multiple Cameras

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Distributed Embedded Smart Cameras

Abstract

Detecting and tracking people using cameras is a basic task in many applications such as video surveillance and smart environment. In this chapter, we review approaches that detect and track targets using a single camera. After that, we explore the approaches that fuse multiple sources of information to enable tracking in a camera network. At last, we show an application that estimates the occupancy in a smart room.

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References

  1. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, vol 1. IEEE, New Jersy, pp 886–893

    Google Scholar 

  2. Du W, Piater J (2007) Multi-camera people tracking by collaborative particle filters and principal axis-based integration. In: Computer vision-ACCV 2007. Springer, Heidelberg, pp 365–374

    Google Scholar 

  3. Farnebäck G (2003) Two-frame motion estimation based on polynomial expansion. In: Image analysis. Springer, Heidelberg, pp 363–370

    Google Scholar 

  4. Kellogg T, Truesdale R, Kellogg L, Osterman MDW, Brady H, Martin H, Webb P (1981) A computer algorithm for reconstructing a scene from two projections. Nature 293:133

    Article  Google Scholar 

  5. Ni Z. Sunderrajan S, Rahimi A, Manjunath B (2010) Distributed particle filter tracking with online multiple instance learning in a camera sensor network. In: 17th IEEE international conference on image processing (ICIP), 2010. IEEE, New Jersy, pp 37–40

    Google Scholar 

  6. Ojala T, Pietikainen M, Harwood D (1994) Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR international conference on pattern recognition, 1994. Conference A: computer vision and image processing, vol 1. IEEE, New Jersy, pp 582–585

    Google Scholar 

  7. Okuma K, Taleghani A, De Freitas N, Little JJ, Lowe DG (2004) A boosted particle filter: multitarget detection and tracking. In: ECCV. Springer, Heidelberg, pp 28–39

    Google Scholar 

  8. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web

    Google Scholar 

  9. Pérez P, Hue C, Vermaak J, Gangnet M (2002) Color-based probabilistic tracking. In: ECCV. Springer, Heidelberg, pp 661–675

    Google Scholar 

  10. Viola P, Jones MJ (2004) Robust real-time face detection. IJCV 57(2):137–154

    Article  Google Scholar 

  11. Zhang C, Platt JC, Viola PA (2005) Multiple instance boosting for object detection. In. Advances in neural information processing systems, pp 1417–1424

    Google Scholar 

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Correspondence to Zixuan Wang .

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Wang, Z., Aghajan, H. (2014). Tracking by Detection Algorithms Using Multiple Cameras. In: Bobda, C., Velipasalar, S. (eds) Distributed Embedded Smart Cameras. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7705-1_8

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  • DOI: https://doi.org/10.1007/978-1-4614-7705-1_8

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7704-4

  • Online ISBN: 978-1-4614-7705-1

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