Presentation on theme: "Activity Recognition Ram Nevatia Presents work of F. Lv, P. Natarajan and V. Singh Institute of Robotics and Intelligent Systems Computer Science Department."— Presentation transcript:
Activity Recognition Ram Nevatia Presents work of F. Lv, P. Natarajan and V. Singh Institute of Robotics and Intelligent Systems Computer Science Department Viterbi School of Engineering University of Southern California
Activity Recognition: Motivation Is the key content of a video (along with scene description) Useful for Monitoring (alerts) Indexing (forensic, deep analysis, entertainment…) HCI..
Issues in Activity Recognition Inherent ambiguities of 2-D videos Variations in image/video appearance due to changes in viewpoint, illumination, clothing (texture)…. Variations in style: different actors or even the same actor at different times Reliable detection and tracking of objects, especially those directly involved in activities Temporal segmentation Most work assumes single activity in a given clip Recognition of novel events
Possible Approaches Match video signals directly Dynamic time warping Extract spatio-temporal features, classify based on them Bag of words, histograms, clouds… Work of Laptev et al Most earlier work assumes action segmentation (detection vs classification) Andrews talk on use of localization and tracking Structural Approach Based on detection of objects, their tracks and relationships Requires ability to perform above operations
Event Hierarchy Composite Events Compositions of other, simpler events. Composition is usually, but not necessarily, a sequence operation, e.g. getting out of a car, opening a door and entering a building. Form a natural hierarchy (or lattice) Primitive events: those we choose not to decompose, e.g. walking Recognized directly from observations, Graphical models, such as HMMs and CRFs are natural tools for recognition of composite events.
Key Ideas –Only need a few primitive actions in any domain. –Sign Language: Moves and Holds. –Human Pose Articulation: Rotate, Flex and Pause. –Rigid Objects (cars, people): Translate, Rotate, Scale. –Can be represented symbolically using formal rules. –Composite Actions can be represented as combinations of the primitive actions. –Handle uncertainty and error in video by mapping rules to a Graphical Models. –HMM. –DBN. –CRF.
Graphical Models A network, normally used to represent temporal evolution of a state Next state depends only on previous state; observation depends only on current state, single state variable Typical task is to estimate most likely state sequence given an observation sequence- Viterbi algorithm A CRF An HMM
Mid vs Near Range Mid-range Limbs of human body, particularly the arms, are not distinguishable Common approach is to detect and track moving objects and make inferences based on trajectories Near-range Hands/arms are visible; activities are defined by pose transitions, not just the position transitions Pose tracking is difficult; top-down methods are commonly used
Mid-Range Example Example of abandoned luggage detection Based on trajectory analysis and simple object detection/recognition Uses a simple Bayesian classifier and logical reasoning about order of sub-events Tested on PETS, ETISEO and TRECVID data
Top-Down Approaches Bottom-up methods remain slow, are not robust; many methods are based on use of multiple video streams An alternative is of top-down approaches where processing is driven by event models Simultaneous Tracking and Action Recognition (STAR) In analogy with SLAM in robotics Provides action segmentation, in addition to recogntion Closed-world assumption Current work limited to single actor actions
Activity Recognition w/o Tracking Input sequence ………… 3D body pose ………… check watch punchkickpick upthrow + Action segments
Viewpoint change & pose ambiguity (with a single camera view) Difficulties Spatial and temporal variations (style, speed)
Key Poses and Action Nets Key poses are determined from MoCap data by an automatic method that computes large changes in energy; key poses may be shared among different actions
Experiments: Training Set 15 action models 177 key poses 6372 nodes in Action Net
Action Net: Apply constraints 0o0o 10 o …
Experiments: Test Set 50 clips, average length 1165 frames 5 viewpoints 10 actors (5 men, 5 women)
A Video Result extracted blob & ground truth with action net without action net original frame
Working with Natural Environments Reduce reliance on good foreground segmentation Key poses may not be discriminative enough w/o accurate segmentation; include models for motion between key poses More general graphical models that include Hierarchy Transition probabilities may depend on observations Observations may depend on multiple states Duration models (HMMs imply an exponential decay) Remove need for MoCap data to acquire models
Dynamic Bayesian Action Network –Map action models to a Dynamic Bayesian Network –Decompose a composite action into a sequence of primitive actions –Each primitive is expressed in a function form f pe (s,s,N). –Maps current state s to next state s given parameters N. –Assume a known, finite set of functions f for primitives.
Inference Overview Given a video, obtain initial state distribution with start key pose for all composite actions For each current state: Predict the primitive based on the current duration Predict a 3D pose given the primitive and current duration Collect the observation potential of the pose using foreground overlap and difference image Obtain the best state sequence using dynamic programming (Viterbi Algorithm) Features used to match models with observations If foreground can be extracted reliably, then we can use blob shape properties; otherwise, use edge and motion flow matching
Obtain state distributions by matching poses sampled from action models Infer the action by finding the max likelihood state sequence, Pose Tracking & Action Recognition Inference Algorithm
Observations Foreground overlap with full body model, Difference Image overlap with body parts in action Grid-of-centroids to match foreground blob with pose
Results From CVPR08 paper
Action Learning Involves two problems Model Learning: Learning parameters N in the primitive event definition f pe (s,s,N). –Key Pose Annotation and Lifting. –Pose Interpolation Feature Weight Learning: Learning the weights w k of the different potentials.
KeyPose Annotation and 3D Lifting
Pose Interpolation All limb motions can be expressed in terms of Rotate(part,axis,q). We need to learn axis and q. Simple to do given the start and end joints of part.
Feature Weight Learning Feature weight estimation as minimization of a log- likelihood error function. Learn the weights using Voted Perceptron Algorithm Requires fully labeled training data -> not available. We propose an extension to deal with partial annotations. Latent State Voted Percepton
Experiments Tested method on 3 datasets Weizmann dataset Gesture set with arm gestures Grocery Store set with full body actions DatasetTrain:Test Ratio Action Recognition (% accuracy) 2D Tracking (% error) Speed (fps) Weizmann3: Gesture3: Grocery Store1:
Weizmann Dataset Popular dataset for action recognition 10 full body actions from 9 actors Each video has multiple instance of one action Train:TestRecognition Accuracy Jhuang et al 6:398.8 Space-Time Shapes 8: Fathi Et al 8: Sun et al 3:687.3 DBAN1:896.7 DBAN3:699.5
Gesture Dataset 5 instances of 12 gestures from 8 actors. Indoor lab setting. 500 instances of all actions. 852x480 pixel resolution, person height: pix.
Grocery Store Dataset Videos of 3 actions collected from a static camera. 16 videos from 8 actors, performed at pan angles. Actor height varies from pixels, in 852x480 resolution videos.
Incorporating Better Descriptors Previous work based on weak lower-level analysis We can also evaluate 2D part models Dynamic Bayesian Action Network with Part Model
Experiments Hand gesture dataset in an Indoor lab 5 instances of 12 gestures from 8 actors, total of 500 action segments Evaluation metrics Recognition rate over all action segments 2D pose tracking as average 2D part accuracy over 48 randomly selected instances DatasetTrain:Test Ratio Recognition (% accuracy) 2D Tracking (% accuracy) DBAN-FGM1: (89.94) DBAN-Parts1: (92.66)
Summary and Conclusions Structural approach to activity recognition offers many attractions and challenges Results are descriptive but detecting and tracking objects is challenging Hierarchical representation is natural and can be used to reduce complexity Good bottom-up analysis remains a key to improved robustness Concept of novel or anomalous events remains difficult to formalize