CYKLS: Detect Pedestrian’s Dart Focusing on an Appearance Change

Masahiro Ogawa, Hideo Fukamachi, Ryuji Funayama, and Toshiki Kindo

Abstract. We propose a new method for detecting “pedestrians’ dart” to support drivers cognition in real traffic scenario. The main idea is to detect sudden appearance change of pedestrians before their consequent actions happen. Our new algorithm, called “Chronologically Yielded values of Kullback-Leibler divergence between Separate frames” (CYKLS), is a combination of two main procedures: (1) calculation of appearance change by Kullback-Leibler divergence between descriptors in some time interval frames, and (2) detection of non-periodic sequence by a new smoothing method in the field of time series analysis. We can detect pedestrians’ dart with 22% Equal Error Rate, using a dataset which includes 144 dart scenes.

LNCS 7584, p. 556 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012