Looking at People (LAP) is a challenging area of research that deals with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others. Any scenario where the visual or multi-modal analysis of people takes a is of interest within the field of Looking at People.
Because of huge space of human configurations, human analysis is a difficult Computer Vision and Machine Learning problem that involves dealing with several distortions: illumination changes, partial occlusions, changes in the point of view, rigid and elastic deformations, or high inter and intra-class variability, just to mention a few. Even with the high difficulty of the problem, modern Computer Vision and Machine Learning techniques deserve further attention.
Several subareas of LAP have been recently defined, such as Affective Computing, Social Signal Processing, Human Behavior Analysis, or Social Robotics. The effort involved in this area of research will be compensated by its potential applications: TV production, home entertainment (multimedia content analysis), education purposes, sociology research, surveillance and security, improved quality life by means of monitoring or automatic artificial assistance, etc.
This page summarizes all previous, current, and upcoming events organized by the ChaLearn LAP team, including international workshops, competitions, special issues, books and other publications related to ChaLearn LAP events. Associated databases and their state-of-the-art results are also available.
We would like to thank all the hundreds of participants that joined our events and that considerably help to advance in both theory and practice of LAP. We thank our sponsors, including Microsoft Research, Google, NVIDIA Corportation, Disney Research, Facebook, and Amazon, among others.
Sergio Escalera, Xavier Baro, Hugo Jair Escalante, Isabelle Guyon. ChaLearn Looking at People: A Review of Events and Resources. Proceedings of the The 2017 International Joint Conference on Neural Networks (IJCNN 2017), IEEE, 2017.