Person and Vehicle Tracking in Surveillance Video

  title={Person and Vehicle Tracking in Surveillance Video},
  author={Andrew Miller and Arslan Basharat and Brandyn Allen White and Jingen Liu and Mubarak Shah},
This evaluation for person and vehicle tracking in surveillance presented some new challenges. The dataset was large and very high-quality, but with difficult scene properties involving illumination changes, unusual lighting conditions, and complicated occlusion of objects. Since this is a well-researched scenario [1], our submission was based primarily on our existing projects for automated object detection and tracking in surveillance. We also added several new features that are practical… 
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  • G. Phadke
  • Engineering
    2011 International Conference on Communications and Signal Processing
  • 2011
This paper investigates how to improve the robustness of visual tracking method for multiple target tracking with occlusion handling and proposes weighted fragment based mean shift with Kalman filter with the consideration of color features of the target.
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Object tracking is a challenging task in surveillance and activity analysis and video transport has technical challenge when the wireless transmissions require high data rate and low latency.
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  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2005
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  • O. JavedK. ShafiqueM. Shah
  • Environmental Science, Computer Science
    Workshop on Motion and Video Computing, 2002. Proceedings.
  • 2002
This method provides the solution to some of the common problems that are not addressed by most background subtraction algorithms, such as fast illumination changes, repositioning of static background objects, and initialization of background model with moving objects present in the scene.
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  • B. WhiteM. Shah
  • Computer Science
    2007 IEEE International Conference on Multimedia and Expo
  • 2007
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  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2005
An object detection scheme that has three innovations over existing approaches that is based on a model of the background as a single probability density, and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph.