1. REU Week 3: Real-Time Video Anomaly Detection
Project Leader:Praveen Tirupattur
Urvi Gianchandani

2. Datasets
Papers
Experiment
Ideas
Next Steps
Overview

3. Datasets UCSD Ped1 & Ped2 CUHK Avenue Subway (Entrance & Exit)
ShanghaiTech Campus

4. Abnormal Event Detection at 150 FPS in MATLAB
Cewu Lu, Jianping Shi, Jiaya Jia (1)
Sparse combination learning: sparsity coded directly as combination of basis vectors
Training: learn dictionary by adding sparse combinations
Testing: use reconstruction error to detect anomalies

5. Weixin Luo, Wen Liu, Shenghua Gao (2)
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
Weixin Luo, Wen Liu, Shenghua Gao (2)
Temporally coherent sparse coding: similar neighboring frames will have similar sparse coefficients

6. Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
Yong Shean Chong, Yong Haur Tay (3)
Spatial encoder → temporal encoder-decoder (3 ConvLSTMs) → spatial decoder
Spatial: extract features
Temporal: learn temporal features of encoded spatial structure

7. AUC Comparisons Ped1 Ped2 Avenue Cewu Lu (1) 91.8 -- Weixin Luo (2)
92.2
81.7
Yong Shean Chong (3)
89.9
87.4
80.3

8. Experiment: Simeple Autoencoder

9. Ideas
Learn normal feature of specific object within a scene - Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video (4)
Use optical flow(reconstruction) and patch-based generator to localize spatial features

10. Running experiments on datasets
Exploring optical flow
Next Steps

11. References http://shijianping.me/abnormal_iccv13.pdf