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Adaptive Resource Management for Sensor Fusion in Visual Tracking

Chapter
Part of the KAIST Research Series book series (KAISTRS)

Abstract

Sensor fusion for visual tracking is attractive since the integration of multiple sensors and/or features with different characteristics has potential to improve tracking performance. However, there exist several critical limitations to sensor fusion techniques: (1) the measurement cost increases typically as many times as the number of sensors, (2) it is not straightforward to quantify the confidence of each source and give each sensor a proper weight for state estimation, and (3) there is no principled algorithm for dynamic resource allocation to achieve better performance. We describe a method to combine information from multiple sensors and estimate the current tracker state by using a mixture of sequential Bayesian filters (e.g., particle filter)—one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, multiple sensors interact to determine an appropriate sensor for each particle dynamically; each particle is allocated to only one of the sensors for measurement and a different number of particles may be assigned to each sensor as a result. The level of the contribution of each sensor changes dynamically based on its prior information and relative measurement confidence. We apply this technique to visual tracking problems with multiple cameras or multiple features, and demonstrate its effectiveness through tracking results in real videos.

Keywords

Visual tracking Resource allocation Sensor fusion Multiple cameras Multiple features Kernel-based bayesian filtering Mixture model 

Notes

Acknowledgment

This work is supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as the Global Frontier Project.

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPOSTECHPohangKorea
  2. 2.Google Inc.Mountain ViewUSA
  3. 3.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

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