Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abnormal Behavior Recognition Using Self-Adaptive Hidden Markov Models
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Dense, robust, and accurate motion field estimation from stereo image sequences in real-time
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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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.