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Unusual Activity in Video

This video, presented by Wei Pan, delves into the innovative methods for detecting unusual activities in video footage. It covers derivative filters applied to signal processing within one-minute intervals, examining co-occurrence matrices and cluster analysis. Key insights into embedding techniques reveal how to define weights between points to emphasize proximity among unusual clusters. The video elaborates on K-Means, inter-cluster similarity, and the importance of understanding embeddings in the context of video analytics. Join us for a deep dive into advanced video analysis methodologies.

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Unusual Activity in Video

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  1. Unusual Activity in Video Presented by wei pan

  2. Blackjack

  3. Video

  4. Keywords for Video Left to Middle: Middle to Right:

  5. Derivative Filters in 1 Minute Signal * Gt

  6. Keywords for Video Left to Middle: Middle to Right: Histogram

  7. Keywords for Video

  8. Co-occurance Matrix

  9. CP • Why not clustering on C? • Intuition Behind

  10. Embedding • Embedding • Given some points, define weights between any two points. • Given a plane, for each point, find a place to place it. • So that, in the plane, a pair of points has higher weights are closer.

  11. Embedding

  12. Co-embedding (See EigenMap): • Nodes: all prototypes and video segments • Weight between each node: • W(Prototype, node) • W(Prototype, Prototype)

  13. W(Prototype, Prototype)

  14. K-Means • Inter-Cluster Similarity • Unusual cluster has small Inter-Cluster Similarity

  15. Questions & Ideas • Thank you………………………………..

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