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Event Detection in Continuous Video: An Inference in Point Process Approach (2017)

by Zhen Qin and Christian R. Shelton

Abstract: We propose a novel approach towards event detection in real-world continuous video sequences. The method 1) is able to model arbitrary-order non-Markovian dependencies in videos to mitigate local visual ambiguities, 2) conducts simultaneous event segmentation and labeling, and 3) is time-window free. The idea is to represent a video as an event stream of both high-level semantic events and low-level video observations. In training, we learn a point process model called piecewise-constant conditional intensity model (PCIM) that is able to capture complex non-Markovian dependencies in the event streams. In testing, event detection can be modeled as the inference of high-level semantic events, given low-level image observations. We develop the first inference algorithm for PCIM and show it samples exactly from the posterior distribution. We then evaluate the video event detection task on real-world video sequences. Our model not only provides competitive results on the video event segmentation and labeling task, but also provides benefits including being interpretable and efficient.


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Zhen Qin and Christian R. Shelton (2017). "Event Detection in Continuous Video: An Inference in Point Process Approach." IEEE Transactions on Image Processing, 26(12), 5680-5691. pdf      

Bibtex citation

@article{QinShe17,
   author = "Zhen Qin and Christian R. Shelton",
   title = "Event Detection in Continuous Video: An Inference in Point Process Approach",
   journal = "IEEE Transactions on Image Processing",
   month = dec,
   year = 2017,
   volume = 26,
   number = 12,
   pages = "5680--5691",
}

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