Proper modeling of dynamic environments is a core task in the field of intelligent vehicles. The most common approaches involve the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which spatial occupancy is tracked at a sub-object level. In this paper, we present the Conditional Monte Carlo Dense Occupancy Tracker, a generic spatial occupancy tracker, which infers dynamics of the scene through an hybrid representation of the environment, consisting of static occupancy, dynamic occupancy, empty spaces and unknown areas. This differentiation enables the use of state specific models (classic occupancy grid for motion-less components, set of moving particles for dynamic occupancy) as well as proper confidence estimation and management of data-less areas. The approach leads to a compact model that drastically improves the accuracy of the results and the global efficiency in comparison to previous methods.
Conditional Monte Carlo Dense Occupancy Tracker
2015-09-01
610882 byte
Conference paper
Electronic Resource
English
Further Evaluation of an Autonomous CCD Star Tracker by the Monte Carlo Method
British Library Online Contents | 1997
|General Introduction to Monte Carlo and Multi-level Monte Carlo Methods
Springer Verlag | 2018
|Evaluation of an appearance-based 3D face tracker using dense 3D data
British Library Online Contents | 2008
|