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Week8. Fatemeh Yazdiananari. Accomplished tasks :. Fixed the issues with classifiers We retrained SVMs with the new UCF101 histograms On temporally untrimmed videos: Three test scenarios: Temporal trimming of validation videos (baseline) Whole-video histogram
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Week8 FatemehYazdiananari
Accomplished tasks: • Fixed the issues with classifiers • We retrained SVMs with the new UCF101 histograms • On temporally untrimmed videos: • Three test scenarios: • Temporal trimming of validation videos (baseline) • Whole-video histogram • Sliding window: uniform windows, regardless of the content • Sliding window: uniform windows, aligned with the content • Max pooling on sliding windows • A - B: Quantified impact of temporal trimmed • D - C: Quantified impact of alignment of windows • E - A: Quantified impact of having multiple instances of one action
Accomplished tasks: • Fixed the issues with classifiers • We retrained SVMs with the new UCF101 histograms • On temporally untrimmed videos: • Three test scenarios: • Temporal trimming of validation videos (baseline) • Whole-video histogram • Sliding window: uniform windows, regardless of the content • Sliding window: uniform windows, aligned with the content • Max pooling on sliding windows • A - B: Quantified impact of temporal trimmed • D - C: Quantified impact of alignment of windows • E - A: Quantified impact of having multiple instances of one action ✔ ✔ ✔ ✔
Action Recognition Overview • Extracted UCF101 DTF features • Extracted histograms of UCF101 DTF features • Trained Binary SVM using Split method • split 1 • Tested Binary SVM with Validation Set • Whole-Video histogram • Extracted histograms of 15 validation videos • Sliding Window: uniform window, regardless of content • Determined 10 second slices of the 15 validation videos • Extracted histograms for those 10 second slices
Results Predict GT Whole-Video
Results Predict GT Whole-Video
Results Predict GT Predict GT Sliding Window Whole-Video