CAR (Complex Activity Recognition) module

CAR(Complex Activity Recognition), written in C++ , is designed for analyzing video content . CAR is able to recognize events such as 'falling', 'walking' of a person. CAR divides the work-flow of a video processing into several separated modules, such as acquisition, segmentation, up to activity recognition. Each module has a specific interface, and different plugins (corresponding to algorithms) can be implemented for a same module. We can easily build new analyzing systems thanks to this set of plugins. The order we can use those plugins and their parameters can be changed at run time and the result visualized on a dedicated GUI. This platform has many more advantages such as easy serialization to save and replay a scene, portability to Mac, Windows or Linux, and easy deployment to quickly setup an experimentation anywhere. CAR takes different kinds of input: RGB camera, depth sensor for online processing; or image/video files for offline processing.

This generic architecture is designed to facilitate:

  1. Integration of new algorithms into CAR;
  2. Sharing of the algorithms among the Stars team. Currently, 15 plugins are available, covering the whole processing chain. Some plugins use the OpenCV library.

The plugins cover the following research topics:

  • algorithms : 2D/3D mobile object detection, camera calibration, reference image updating, 2D/3D mobile object classification, sensor fusion, 3D mobile object classification into physical objects (individual, group of individuals, crowd), posture detection, frame to frame tracking, long-term tracking of individuals, groups of people or crowd, global tacking, basic event detection (for example entering a zone, falling...), human behaviour recognition (for example vandalism, fighting,...) and event fusion; 2D & 3D visualisation of simulated temporal scenes and of real scene interpretation results; evaluation of object detection, tracking and event recognition; image acquisition (RGB and RGBD cameras) and storage; video processing supervision; data mining and knowledge discovery; image/video indexation and retrieval.
  • languages : scenario description, empty 3D scene model description, video processing and understanding operator description;
  • knowledge bases : scenario models and empty 3D scene models;
  • learning techniques for event detection and human behaviour recognition;
Contact:François Brémond - This email address is being protected from spambots. You need JavaScript enabled to view it., Matias Marin - This email address is being protected from spambots. You need JavaScript enabled to view it., Carlos Fernando Crispim-Junior - This email address is being protected from spambots. You need JavaScript enabled to view it.

See alsohttps://team.inria.fr/stars/software/car-complex-activity-recognition-component-installation/


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