Trainable classifier-fusion schemes: An application to pedestrian detection
Oswaldo Ludwig, David Delgado‐Gómez, Valter Gonçalves, Urbano Nunes · 2009
This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the effectiveness of our methods regarding false positive rate, AUC, and accuracy, which reached 96.67%.