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Video Surveillance E6998 -007 Senior/Feris/Tian 1 Behavior Analysis Rogerio Feris IBM TJ Watson Research Center

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Presentation on theme: "Video Surveillance E6998 -007 Senior/Feris/Tian 1 Behavior Analysis Rogerio Feris IBM TJ Watson Research Center"— Presentation transcript:

1 Video Surveillance E Senior/Feris/Tian 1 Behavior Analysis Rogerio Feris IBM TJ Watson Research Center

2 Video Surveillance E Senior/Feris/Tian 2 Outline Motivation Action Recognition Template-Based Approaches State-Space Approaches Detecting Suspicious Behavior

3 Video Surveillance E Senior/Feris/Tian 3 Motivation Action Recognition in Surveillance Video Detecting people fighting Falling person detection

4 Video Surveillance E Senior/Feris/Tian 4 Motivation Detecting suspicious behavior [Boiman and Irani, 2005] Fence Climbing

5 Video Surveillance E Senior/Feris/Tian 5 Find all locations where objects enter or exit (green) Find all normal routes between these locations- average path and observed deviations. Motivation

6 Video Surveillance E Senior/Feris/Tian 6 Tracks anomalies (not matching trained routes) Motivation

7 Video Surveillance E Senior/Feris/Tian 7 Motivation Long-term reasoning / object interaction [Ivanov and Bobick, 2000] Car/person interactions (e.g., car picking up a person)

8 Video Surveillance E Senior/Feris/Tian 8 Challenges Strong appearance variation in semantically similar events (e.g., people performing actions with different clothing Viewpoint Variation Duration of the action / frame rate Action segmentation – determining beginning and end of the action

9 Video Surveillance E Senior/Feris/Tian 9 Outline Motivation Action Recognition Template-Based Approaches State-Space Approaches Detecting Suspicious Behavior

10 Video Surveillance E Senior/Feris/Tian 10 Action Recognition – Template-Based Motion History Image (MHI): Scalar-valued image where brighter pixels correspond to more recently moving pixels Temporal Templates [Bobick and Davis, 1996] Binary image indicating regions of motion

11 Video Surveillance E Senior/Feris/Tian 11 Action Recognition – Template-Based Motion History Image (MHI): Scalar-valued image where brighter pixels correspond to more recently moving pixels Temporal Templates [Bobick and Davis, 1996]

12 Video Surveillance E Senior/Feris/Tian 12 Action Recognition – Template-Based At the current frame, statistical descriptors based on moments (translation and scale invariant) are extracted from the current MHI and matched against stored exemplars for classification Three actions: sitting, arm waving, and crouching. View-based approach to handle camera view changes. Problems with ambiguities, occlusions, poor motion segmentation Temporal Templates [Bobick and Davis, 1996]

13 Video Surveillance E Senior/Feris/Tian 13 Action Recognition – Template-Based Recognizing Action at a Distance [Efros et al, ICCV03] 3-pixel man Blob tracking vast surveillance literature 300-pixel man Limb tracking e.g. Yacoob & Black, Rao & Shah, etc.

14 Video Surveillance E Senior/Feris/Tian 14 The 30-Pixel Man Action Recognition – Template-Based Recognizing Action at a Distance [Efros et al, ICCV03]

15 Video Surveillance E Senior/Feris/Tian 15 Action Recognition – Template-Based Appearance versus Motion Recognizing Action at a Distance [Efros et al, ICCV03]

16 Video Surveillance E Senior/Feris/Tian 16 Tracking Simple correlation-based tracker User-initialized Figure-centric Representation

17 Video Surveillance E Senior/Feris/Tian 17 input sequence Explain novel motion sequence by matching to previously seen video clips For each frame, match based on some temporal extent Challenge: how to compare motions? motion analysis run walk left swing walk right jog database Action Recognition – Template-Based Recognizing Action at a Distance [Efros et al, ICCV03]

18 Video Surveillance E Senior/Feris/Tian 18 Spatial Motion Descriptor Image frame Optical flow blurred

19 Video Surveillance E Senior/Feris/Tian 19 t … … … … Sequence A Sequence B Temporal extent E B frame-to-frame similarity matrix A motion-to-motion similarity matrix A B I matrix E E blurry I E E Two person running sequences - periodic behavior

20 Video Surveillance E Senior/Feris/Tian 20 Action Recognition – Template-Based Recognizing Action at a Distance [Efros et al, ICCV03] Classification is done for each frame. The spatial-temporal descriptor centered at the current frame is matched against the database of actions (previously stored spatial-temporal descriptors). For each frame of the probe sequence, the maximum score in the corresponding row of the motion-to-motion similarity matrix (between probe and one sequence of the database) will indicate the best match to the spatial-temporal descriptor centered at this frame. K-nearest neighbors is used to determine the action. Good results were demonstrated in sequences related to tennis, soccer, and dancing.

21 Video Surveillance E Senior/Feris/Tian 21 2D Skeleton Transfer The database is annotated with 2D joint positions After matching, data is transfered to novel sequence Input sequence: Transferred 2D skeletons: Action Recognition – Template-Based Recognizing Action at a Distance [Efros et al, ICCV03]

22 Video Surveillance E Senior/Feris/Tian 22 Actor Replacement Show Video GregWordCup.avi Action Recognition – Template-Based Recognizing Action at a Distance [Efros et al, ICCV03]

23 Video Surveillance E Senior/Feris/Tian 23 Action Recognition – Template-Based Proposed for image similarity. Action detection is a particular application Local Self-Similarities [Shechtman and Irani, CVPR07] How to measure similarity in these images?

24 Video Surveillance E Senior/Feris/Tian 24 Action Recognition – Template-Based Local Self-Similarities [Shechtman and Irani, CVPR07]

25 Video Surveillance E Senior/Feris/Tian 25 Action Recognition – Template-Based Local Self-Similarities [Shechtman and Irani, CVPR07] The descriptor implicitly handles the similarity between people wearing different clothes. Also, the spatial-temporal log-polar binning allows for better matching under different action durations / frame rate.

26 Video Surveillance E Senior/Feris/Tian 26 Action Recognition – Template-Based Complex actions performed by different people wearing different clothes with different backgrounds, are detected with no prior learning, based on a single example clip. Local Self-Similarities [Shechtman and Irani, CVPR07]

27 Video Surveillance E Senior/Feris/Tian 27 Action Recognition – Template-Based Spatial-Temporal Bag of Words [Niebles et al, CVPR06]

28 Video Surveillance E Senior/Feris/Tian 28 Outline Motivation Action Recognition Template-Based Approaches State-Space Approaches Detecting Suspicious Behavior

29 Video Surveillance E Senior/Feris/Tian 29 Action Recognition – State-Space Hidden Markov Models [Rabiner, 1989]

30 Video Surveillance E Senior/Feris/Tian 30 Action Recognition – State-Space Hidden Markov Models [Rabiner, 1989]

31 Video Surveillance E Senior/Feris/Tian 31 Action Recognition – State-Space Hidden Markov Models [Rabiner, 1989] Three Basic Problems: Forward-Backward Algorithm

32 Video Surveillance E Senior/Feris/Tian 32 Action Recognition – State-Space Hidden Markov Models [Rabiner, 1989] Three Basic Problems: Viterbi Algorithm

33 Video Surveillance E Senior/Feris/Tian 33 Action Recognition – State-Space Hidden Markov Models [Rabiner, 1989] Three Basic Problems: Baum-Welch Algorithm

34 Video Surveillance E Senior/Feris/Tian 34 Action Recognition – State-Space Hidden Markov Models [Rabiner, 1989] Action Recognizer: Learn an HMM model for each action in the database (e.g., HMM for running, HMM for fighting, etc.) – Baum-Welch algorithm Given an action sequence, compare it with all HMMs in the database and select the one which best explains the probe sequence – Forward-Backward algorithm

35 Video Surveillance E Senior/Feris/Tian 35 Action Recognition – State-Space [Yamato et al, 1992] - First application of HMMs for gesture recognition (for recognizing tennis strokes) From there on HMMs have been extensively applied in many gesture recognition problems (Sign Language Recognition, Head Gesture, etc.) Many variations have been proposed (see e.g., coupled HMMs). More recently, Conditional Random Fields (CRFs) have proven to be very successful to model human motion [ Sminchisescu et al, ICCV 2005]

36 Video Surveillance E Senior/Feris/Tian 36 Action Recognition – State-Space Modeling Interactions with Stochastic Grammars [Ivanov and Bobick, 2000] Recognize actions with larger temporal range Two-Stage Approach: Detection of low-level discrete events (e.g., using HMMs or tracking) Action Recognition using Stochastic Grammars

37 Video Surveillance E Senior/Feris/Tian 37 Action Recognition – State-Space Modeling Interactions with Stochastic Grammars [Ivanov and Bobick, 2000] Background: Earley Parsing for Context-free Grammars See description in wikipedia Three main steps: Prediction, Scanning, Completion

38 Video Surveillance E Senior/Feris/Tian 38 Earley Parsing Example

39 Video Surveillance E Senior/Feris/Tian 39 Action Recognition – State-Space Modeling Interactions with Stochastic Grammars [Ivanov and Bobick, 2000] Probabilistic Earley Parsing Production rules are augmented with probabilities Parse tree with highest probability is generated [Stolcke, Bayesian Learning of Probabilistic Language Models,1994]

40 Video Surveillance E Senior/Feris/Tian 40 Action Recognition – State-Space Modeling Interactions with Stochastic Grammars [Ivanov and Bobick, 2000] Car/Person Interaction Low-level discrete event detection Track moving blobs Generate events: {person,car}+{enter,found,exit,lost,stopped}

41 Video Surveillance E Senior/Feris/Tian 41 Modeling Interactions with Stochastic Grammars [Ivanov and Bobick, 2000]

42 Video Surveillance E Senior/Feris/Tian 42 Outline Motivation Action Recognition Template-Based Approaches State-Space Approaches Detecting Suspicious Behavior

43 Video Surveillance E Senior/Feris/Tian 43 Suspicious Behavior Problem: given a few regular examples, compute the likelihood of a new observation Detecting Irregularities [Boiman and Irani, ICCV 2005] DatabaseQuery Construct the likelihood using chuncks of data from the examples. Large matching chunks imply large likelihood.

44 Video Surveillance E Senior/Feris/Tian 44 Suspicious Behavior Problem: given a few regular examples, compute the likelihood of a new observation Detecting Irregularities [Boiman and Irani, ICCV 2005] Database Construct the likelihood using chuncks of data from the examples. Large matching chunks imply large likelihood. Query

45 Video Surveillance E Senior/Feris/Tian 45 Suspicious Behavior Detecting Irregularities [Boiman and Irani, ICCV 2005]

46 Video Surveillance E Senior/Feris/Tian 46 Suspicious Behavior [Zhong et al, Detecting Unusual Activity in Video, CVPR04] See Also: [Stauffer and Grimson, Learning patterns of activity using real-time tracking, 2000] [Lei Chen et al, Robust and fast similarity search for moving object trajectories, 2005] Motion Trajectory Behavior:


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