Multimodal Behavior Analysis in the Wild

1. Multimodal behavior analysis in the wild: an introduction
2. Auditory-motor perception in natural environments
3. An integrated audio-visual framework for assisting visually impaired users
4. Audio-visual person identification with wearable cameras
5. Understanding social relationships in egocentric vision
6. Lifelogging through egocentric vision
7. A study of speech distortion conditions in real scenarios for speech processing applications
8. Understanding the scene from a first-person perspective
9. Behavior analysis from wearable sensors
10. Wearable systems for improving museum experience
11. Animal behavior in museums
12. Separating multiple moving sound sources
13. Behaviour analysis of crowds from fixed and moving cameras
14. Detecting conversational groups in images and sequences: a game-theoretic perspective
15. Audio-visual scene understanding with robots
16. Hirability in the Wild: Analysis of Online Conversational Video Resumes
17. Multimodal open-domain conversations with robotic platforms
18. Deep multimodal fusion for persuasiveness prediction
19. Directions robot: In-the-wild experiences and lessons learned
20. Affective facial computing in the wild
21. Automatic recognition of self-reported and perceived emotions
22. Real-world automatic continuous affect recognition from audiovisual signals
23. Deep audio-visual emotion recognition
24. Deep face attributes in the wild
25. Valence and Arousal estimation in the wild
26. Capturing Order in Social Interactions
27. Human Postural Sway Estimation from Noisy Observations
28. Video Based Emotion Recognition in the Wild using Deep Transfer Learning and Score Fusion
29. Socially-aware group detection
30. Open challenges in recognizing behavior in the wild

Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links.

This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing.

• Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios
• Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources
• Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data

Researchers and graduate students in computer vision, machine learning, pattern recognition, signal processing, and audio processing