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Thomas Swift

Thomas Swift. Week 9. Aladdin Dataset Problems. Original feature representation: Histograms of Dollar Spatio -Temporal features. Problem: Histograms don’t preserve temporal information in video sequence; doesn’t take advantage of strength of DI.

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Thomas Swift

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  1. Thomas Swift Week 9

  2. Aladdin Dataset Problems • Original feature representation: Histograms of Dollar Spatio-Temporal features. • Problem: Histograms don’t preserve temporal information in video sequence; doesn’t take advantage of strength of DI. • DI is very time sensitive (order sensitive in vector case), good at following features throughout time sequence. • Results: Very poor. Continuously classified all queries as same couple of actions.

  3. Aladdin Dataset Continued • New feature representation: concatenated Dollar S-T descriptors at interest points in video frames. • Retains temporal info; should work well with DI. • Problem: RUN TIME! • DI has exponential run time. • L = 1,000; T = 1.3 secs • L = 10,000; T = 60 secs (46 times longer) • L = 100,000; T = 8270 secs = 2.25 hrs (137 times longer) • Vector lengths for video representation generally 500,000-900,000 in length. • 15 event types * 100 training examples per event * 300 queries (20 per event) = unrealistic run time. • Results: Effort abandoned (for now).

  4. Oregon State Football Dataset • 23 videos- split into 20% testing (~5) and 80% training (~18) through K_folds cross validation • Loop through all K_folds testing/training divisions (each video tested once) • 23 videos split into 5 groundtruth classes (play types) • Results: 21/23 classified correctly (91.3%) • Really 21/21, as 2 of the 5 classes only have 1 example, and so only exist in training or testing set, and so can’t be matched to its own class

  5. More Extensive Testing • Rather than splitting dataset into training and testing, compute DI between each play and all other plays but itself; find highest score. • Results: 12/23(21)- 52.1%(57.1%) • Further extension: from computed DI scores, group scores by play type and take means; find highest mean score among types. • Results: 7/23(21)- 30.4%(33.3%) • Problem: Play type 3 is an anomaly- has run & pass components, only 1 example • Of 14 misclassified plays, 13 were classified as type 3 • Of these 13, second best choice was correct for 6 of them • For pass queries, DI scores were higher for all pass plays than all run plays

  6. Next Week • Complete registration of plays to static field model, and then recompute optical flow and rerun program (should improve results!). • Salman is implementing paper. • I have obtained C code for that paper. • Do time warping to get uniform vector lengths. • Only using 15-40% of vector in most cases. • Get better (larger and more diverse) dataset. • Get more compact vector representation for videos in Aladdin Dataset. • Subh recommended PCA to reduce dimensionality.

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