Name
Abstract | ||
|---|---|---|
Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is 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 each cell is tracked at a sub-object level. The Bayesian Occupancy Filter [1] is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30×30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IVS.2014.6856554 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
data compression,embedded systems,filtering theory,object detection,object tracking,remotely operated vehicles,sampling methods,DATMO method,cell velocity distributions,data compression,detection and tracking of moving objects,embedded devices,embedded systems,hybrid sampling Bayesian occupancy filter,intelligent vehicle,object model framework,occupancy grid filtering domain | Histogram,Computer vision,Regular grid,Object model,Filter (signal processing),Algorithm,Occupancy,Artificial intelligence,Engineering,Data compression,Occupancy grid mapping,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1931-0587 | 17 | 0.92 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amaury Nègre | 1 | 124 | 8.88 |
| Lukas Rummelhard | 2 | 25 | 2.62 |
| Christian Laugier | 3 | 133 | 14.37 |