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Cascaded Tracking with Incrementally Learned Projections

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Book cover Intelligent Visual Surveillance (IVS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 664))

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Abstract

A convention in visual object tracking is to only favor the candidate with maximum similarity score and take it as the tracking result, while ignore the rest. However, surrounded samples also provide valuable information for target locating, and the combination of their votes can produce more stable results. In this paper, we have proposed a novel method based on the supervised descent method (SDM). We search for the target from multiple start positions and locate it with their votes. For evaluating each predicted descent direction, we have presented a confidence estimating scheme for SDM. To adapt the tracking model to appearance variations, we have further presented an incremental cascaded support vector regression (ICSVR) algorithm for model updating. Experimental results on a recent benchmark demonstrate the superior performance of our tracker against state-of-the-arts.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Lianghua Huang

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  1. Lianghua Huang
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Correspondence to Lianghua Huang .

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Editors and Affiliations

  1. Chinese Academy of Sciences , Beijing, China

    Zhang Zhang

  2. Chinese Academy of Sciences , Beijing, China

    Kaiqi Huang

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© 2016 Springer Nature Singapore Pte Ltd.

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Huang, L. (2016). Cascaded Tracking with Incrementally Learned Projections. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_7

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  • DOI: https://doi.org/10.1007/978-981-10-3476-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3475-6

  • Online ISBN: 978-981-10-3476-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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