Cost-Sensitive Top-Down/Bottom-Up Inference for Multiscale Activity Recognition

  • Mohamed R. Amer
  • Dan Xie
  • Mingtian Zhao
  • Sinisa Todorovic
  • Song-Chun Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

This paper addresses a new problem, that of multiscale activity recognition. Our goal is to detect and localize a wide range of activities, including individual actions and group activities, which may simultaneously co-occur in high-resolution video. The video resolution allows for digital zoom-in (or zoom-out) for examining fine details (or coarser scales), as needed for recognition. The key challenge is how to avoid running a multitude of detectors at all spatiotemporal scales, and yet arrive at a holistically consistent video interpretation. To this end, we use a three-layered AND-OR graph to jointly model group activities, individual actions, and participating objects. The AND-OR graph allows a principled formulation of efficient, cost-sensitive inference via an explore-exploit strategy. Our inference optimally schedules the following computational processes: 1) direct application of activity detectors – called α process; 2) bottom-up inference based on detecting activity parts – called β process; and 3) top-down inference based on detecting activity context – called γ process. The scheduling iteratively maximizes the log-posteriors of the resulting parse graphs. For evaluation, we have compiled and benchmarked a new dataset of high-resolution videos of group and individual activities co-occurring in a courtyard of the UCLA campus.

Keywords

Covariance Gall Tated Time 165s 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mohamed R. Amer
    • 1
  • Dan Xie
    • 2
  • Mingtian Zhao
    • 2
  • Sinisa Todorovic
    • 1
  • Song-Chun Zhu
    • 2
  1. 1.Oregon State UniversityCorvallisUSA
  2. 2.University of CaliforniaLos AngelesUSA

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