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. © 2012 Springer-Verlag.
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CITATION STYLE
Ogawa, M., Fukamachi, H., Funayama, R., & Kindo, T. (2012). CYKLS: Detect pedestrian’s dart focusing on an appearance change. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7584 LNCS, pp. 556–565). Springer Verlag. https://doi.org/10.1007/978-3-642-33868-7_55
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