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Cooperative object detection in road traffic 1 1The research for this paper was financially supported by the Hollósi Ferenc Tudástámogató Alapítvány
- Source :
- IFAC-PapersOnLine. 50:264-269
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Multi-sensor object detection and tracking on a highway scene with radar measurements is presented. The estimation algorithm is the random finite set based Bernoulli filter, working in the Bayesian framework. The recursion for calculating the Bayes estimation is implemented as a particle filter. A method is presented for calculating the likelihoods, suitable for particle filtering performed with moving sensors, assuming additive Gaussian measurement noise. In our approach, for calculating the posterior estimate of the object state, the measurement likelihoods are computed in the state space, instead of the measurement space, by mapping each measurement to the global coordinate system. The map consists of a nonlinear and an affine part. While the affine transformation trivially preserves the Gaussian nature, the nonlinear is well-proven to be approximated as affine too. This approach allows the particles to be drawn directly from the state space, hence the evaluation of the measurement model is not needed, which saves computational power.
- Subjects :
- 020301 aerospace & aeronautics
business.industry
Gaussian
Recursion (computer science)
020206 networking & telecommunications
02 engineering and technology
Object detection
Nonlinear system
Noise
symbols.namesake
0203 mechanical engineering
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
symbols
State space
Computer vision
Artificial intelligence
Affine transformation
business
Particle filter
Mathematics
Subjects
Details
- ISSN :
- 24058963
- Volume :
- 50
- Database :
- OpenAIRE
- Journal :
- IFAC-PapersOnLine
- Accession number :
- edsair.doi...........0401f142d1f22b63c4b16237beca3d42