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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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On-Line Video Event Detection by Constraint Flow.

Suha Kwak, Bohyung Han, Joon Hee Han

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study introduces a new method for video event detection using scenarios, improving efficiency and accuracy. The approach effectively detects complex events by optimizing constraint flows for better video interpretation.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Composite video event detection is challenging due to complex temporal-logical structures.
    • Existing methods struggle with efficiency and handling preprocessing errors in complex event analysis.

    Purpose of the Study:

    • To propose a novel scenario-based approach for describing and detecting composite video events.
    • To develop an efficient and effective on-line event detection algorithm using combinatorial optimization.

    Main Methods:

    • A new scenario description method representing composite events fluently.
    • Automatic generation of constraint flow via scenario parsing.
    • Formulating composite event detection as a constrained discrete optimization problem.

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  • Employing dynamic programming for an on-line event detection algorithm.

    • Main Results:

    • The proposed algorithm significantly reduces the search space for event detection.
    • The method effectively handles preprocessing errors in video analysis.
    • Experimental results on natural videos confirm the algorithm's effectiveness and global optimality.

    Conclusions:

    • The novel scenario-based approach provides a fluent and efficient method for composite video event description and detection.
    • The on-line detection algorithm based on constraint flow and dynamic programming offers a globally optimal solution.
    • This research advances the field of video event analysis with practical implications for real-world applications.