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A Discriminative Key Pose Sequence Model for Recognizing Human
Interactions
Arash Vahdat, Bo Gao, Mani Ranjbar, and Greg Mori
ICCV2011
Goal
Outline
• Introduction• Related methods• Modeling Human Interactions• Single Subject Key Pose Sequence Model• Interaction Key Pose Sequence Model• Learning the parameters• Experiments
Introduction
• This paper focuses on recognizing interactions between individuals.
• The sequences of key poses between peoples will be combined into activity level.
• The activities to be recognized include hugging ,shaking hands,pointing, punching, kicking,pushing each others.
Introduction
• Not every movement or pose by the target is relevant to the activity to be recognized.
• We use an examplar-based model by giving the pose a score to match the key poses.
• When people do some activities, they will do the key poses in a chronological order.
Related methods
• Ryoo and Aggarwal [13]develop a matching kernel that considers spatial and temporal relations between space-time interest points.
• Yao et al. [23] use a Hough transform voting scheme from an interest point representation.
[13] M. Ryoo and J. Aggarwal. Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In ICCV, 2009.[23] A. Yao, J. Gall, and L. Van Gool. A hough transform-based voting framework for action recognition. In CVPR, 2010. 2, 6
Recognition result in [13]
Modeling Human Interactions
• There are four things to know:• 1. Who is involved in the interaction?– Subject or object
• 2. When do the key poses occur? – The interval of the key poses
• 3. How are the key poses executed?– With hand or leg , powerful or weak
• 4. Where are the people when the key poses occur?
Modeling Human Interactions
• we will assume F maximizes a model G that includes the latent variables H:
• The variables H are the answer of the four questions above.
Single Subject Key Pose Sequence Model
Single Subject Key Pose Sequence Model
• We represent each key pose by h:
• Denote K key poses of a sequense by H
Single Subject Key Pose Sequence Model
• Exemplar Matching Link:
• Compute the pose connection strength to the exemplar poses.
Single Subject Key Pose Sequence Model
• Activity-Key Pose Link:
– Compute the sequence similarity in the activity to give a score.
• Direct Root Model:
Interaction Key Pose Sequence Model
• Update the model from individual to two people interaction.
• We should recognize who is subject or object.• The scoring function will be:
Learning the parameters
• Define the scoring function E(x,y):
• Use multiclass linear SVM classifier to find the best parameters.
Experiments
• The UT-Interaction dataset contains videos of 6 classes of human-human interactions.
• Set 1 is with a stationary background,and Set 2 is with slight background movement and camera jitter.
Experiments
Experiments
Experiments
Experiments
Conclusion
• This paper focuses on the key poses method to recognize the interaction.
• The precision is over 90% and outperform other methods in UT-interaction dataset.