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Learning Temporal Regularity in Video Sequence Data and Codes: http://www.ee.ucr.edu/~mhasan/regularity.html Mahmudul Hasan 1 Jonghyun Choi 2 Jan Neumann 2 Amit K. Roy-Chowdhury 1 Larry S. Davis 3 This research is partly supported by NSF grant IIS-1316934and ONR MURI Grant No. N00014-10-1-0934. Anomaly Detection: Comparison with State-of-the-art Methods Visualizing Temporal Regularity Motivation q Watching long hours of uncontrolled videos is extremely tedious No dataset bias compensated Regularities by the General Model More Results are in the paper Applications and Experiments A sample irregular frame Synthesized Regular frame Regularity score A sample irregular frameSynthesized Regular frame Regularity score UCSD Ped2 Dataset UT-AustinSubway-ExitDataset Predicting Near Past and Future CUHK Avenue Dataset UT-AustinSubway-ExitDataset Regularity Scores UT-AustinSubway-ExitDataset CUHK Avenue Dataset UT-Austin Subway-Enter Dataset Feature Based Fully Connected Autoencoder (FConn) End-to-End Fully Convolutional Autoencoder (FConv) UC Riverside 1 Comcast Labs, DC 2 University of Maryland, College Park 3 q We want to segment ‘meaningful’ moments in such videos without supervision Challenges q Learning a classification model of these meaningful (irregular) moments is not trivial because – q Ill defined (anything can be meaningful) q Infrequent (small training data) q Labeling them is expensive. Approach q Use two high capacity generative models - deep neural network based auto-encoders (DNN-AE): q Fully connected DNN-AE on hand-crafted feature q Fully convolutional DNN-AE on frames q Regular input -> Reconstruction cost is small. q Irregular input -> Reconstruction cost is large. Exemplar output of our model when there are irregular motions, the regularity score drops significantly. Our Objective q Learning a generative model for regularity with q Limited supervision required q Ease of learning q Multiple datasets used to train Overview of the Approach Model Architecture Input: HOG+HOF collected around the trajectory of interest point Input Data Layer and Data Augmentation q Input cuboid sizes – 5, 10, and 20 (We use 10) q Large cuboid -> better discrimination and increased running time. q Sliding window size: 10, 20, and 30 with sample rate of stride 1, 2, and 3 respectively. q Sliding windows are moved 2 frames at a time. Training a General Model Regularity Score: s(t) Optimization q LR Scheme: AdaGrad q Init. LR: 0.001 (FConv) and 0.01 (FConn) q Mini-batch size: 1024 (FConv) and 32 (FConn) q Weight initialization: Xavier e(x, y, t)= kI (x, y, t) - f W (I (x, y, t))k 2 e(t)= ⌃ (x,y ) e(x, y, t) s(t)=1 - e(t) - min t e(t) max t e(t)
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