Scene-Specific Pedestrian Detector Using Monte Carlo Framework and Faster R-CNN Deep Model
Ala Mhalla, Thierry Château, Sami Gazzah, Najoua Essoukri Ben Amara · 2016
In this work, we propose a novel approach to automatically specialize a generic pedestrian detector to specific scene by utilizing the sequential Monte Carlo filter and the Faster R-CNN deep model. The main idea is to consider the Faster R-CNN as a function that generates realizations from the probability distribution of the pedestrian to be detected in the target sequence. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized Faster R-CNN estimated in a sequential Bayesian filter framework. The resulting algorithm is compared to the state of the art scene specialization methods on several challenging datasets. The results are very promising.