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 methodologies and technologies when deployed in real world scenarios ‘In the Wild’.
The book focuses on audio and video modalities as well as 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) to high level (personality and emotion recognition), providing insights on how to exploit both the inter-level and the intra-level links between tasks.
This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild, 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 state-of-the-art, limitations, and challenges when trying to extract behavioral cues from ‘in the wild’ real-world scenarios
- Numerous applications of how different behavioral cues have been successfully extracted from different data sources
- Presents 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
- No. of pages:
- © Academic Press 2019
- 1st October 2018
- Academic Press
- Paperback ISBN:
Xavier Alameda-Pineda received his PhD from INRIA and University of Grenoble in2013. He was a post-doctoral researcher at CNRS/GIPSA-Lab and at the University of Trento, in the deep relational learning group. He is a research scientist at INRIA working on signal processing and machine learning for scene and behavior understanding using multimodal data. He is the winner of the best paper award of ACM MM 2015, the best student paper award at IEEE WASPAA 2015 and the best scientific paper award on image, speech, signal and video processing at IEEE ICPR 2016. He is member of IEEE and of ACM SIGMM.
Research Scientist, INRIA
Elisa Ricci is a researcher at FBK and an assistant professor at University of Perugia. She received her PhD from the University of Perugia in 2008. She has since been a postdoctoral researcher at Idiap and FBK, Trento and a visiting researcher at University of Bristol. Her research interests are directed along developing machine learning algorithms for video scene analysis, human behaviour understanding and multimedia content analysis. She is area chair of ACM MM 2016 and of ECCV 2016. She received the IBM Best Student Paper Award at ICPR 2014.
Researcher at FBK, Assistant Professor at the University of Perugia
Nicu Sebe is a full professor at the University of Trento, Italy, where he is leading the research in the areas of multimedia information retrieval and human behavior understanding. He was a general co-chair of FG 2008 and ACM MM 2013, and a program chair of CIVR 2007 and 2010, of ACM MM 2007 and 2011, and of ECCV 2016. He is a program chair of ICCV 2017 and of ICPR 2020, and a general chair of ICMR 2017. He is a senior member of IEEE and ACM and a fellow of IAPR.
Full Professor, University of Trento, Italy