1. Technion - Israel Institute of Technology Department of Electrical Engineering Advanced Topics in Computer Vision Course Presentation By Stav Shapiro

2.  Introduction  Related Work  Sparse Reconstruction & Classification  Video Anomaly Detection  Motivation  Proposed Solution  Results  Discussion

3.  Real world applications?  Surveillance videos

4.  An approach from Signal Processing and Document Classification  Some success in CV applications  Linear Features Linear Algebra

6.  Anomaly Detection in Crowded Scene  MDT – Mixture of Dynamic Textures  A complex and computationally heavy algorithm  Good results for it’s time

7.  Different approach than most model based methods  Uses a small number of ‘Hypotheses’ to describe a training video  Abnormality is an event that cannot be described by the learned hypotheses  State of the art performance

8.  Sparse Representation for Signal Classification  First To use Sparse Representation of a Signal for classification  1 class classification problem  “Huang, Ke, and Selin Aviyente. "Sparse representation for signal classification."  Sparsity In video anomaly detection  “Cong, Yang, Junsong Yuan, and Ji Liu. "Sparse reconstruction cost for abnormal event detection."  Fixed dictionary methods

9.  Online dictionary training  Employs state of the art sparse coding optimization algorithm to improve training time  Still not real time  Zhao, Bin, Li Fei-Fei, and Eric P. Xing. "Online detection of unusual events in videos via dynamic sparse coding."

11.  Learning Phase: Building a Dictionary  Given a ‘Normal’ video sequence  Extract Features, or a ‘Base’  Create a ‘Representation Dictionary’  Sparsity?

16. Indicates that only one Representation is chosen for the reconstruction

20.  3D Gradients from 10x10x5 spatio-tempoal Cuboids at 3 different scales  The Gradients are concatenated and their dimensions are reduced to 100 via PCA  Normalization to mean 0 and variance 1

21.  Training Phase  Given a ‘normal’ video sequence Feature Extraction K Dictionaries Training All the features of a spatio-temporal cube

23. Ped1 Dataset Subway dataset

28.  Pros  Simple, well written  Extensive testing on 3 out of 4 major datasets  General approach that can be basically used for any kind of anomaly detection  Achieves the goal of real time anomaly detection  Cons  May be too simple  Some ad-hoc solutions  Representations may diverge from ‘normal’ after long time (day/night/season)

29.  Online dictionary learning  Can be used as an improved subspace clustering  The basic approach can be used for any type of feature, even for 1D signals.