Hideyuki Mizuta, Takayuki Osogami, et al.
IEEJ Trans. Electron. Inf. Syst.
We present a new approach to lightweight intelligent transportation systems. Our approach does not rely on traditional expensive infrastructures, but rather on advanced machine learning algorithms. It takes images from traffic cameras at a limited number of locations and estimates the traffic over the entire road network. Our approach features two main algorithms. The first is a probabilistic vehicle counting algorithm from low-quality images that falls into the category of unsupervised learning. The other is a network inference algorithm based on an inverse Markov chain formulation that infers the traffic at arbitrary links from a limited number of observations. We evaluated our approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.
Hideyuki Mizuta, Takayuki Osogami, et al.
IEEJ Trans. Electron. Inf. Syst.
Shohei Hido, Tetsuro Morimura
ICPR 2012
Tetsuro Morimura, Sei Kato
ICPR 2012
Tetsuro Morimura, Takayuki Osogami, et al.
NeurIPS 2013