Multi-task learning with over-sampled time-series representation of a trajectory for traffic motion pattern recognition

This paper proposes an efficient feature sampling and multi-task learning scheme for traffic scene analysis, where all classifiers are trained simultaneously by exploiting the correlations among different motion patterns. We make feature descriptors by high dimensional embedding of the time series data for traffic pattern representation. They preserve detailed spatio-temporal information of the underlying event. Pattern specific details are extracted from raw trajectories and embedded into feature descriptors, which ensures their great discriminability. Training data scarcity problem is tackled through amplification of the patterns hidden in raw trajectory via strategic oversampling and employment of joint feature selection procedure while training the models. Experimental results on 4 surveillance datasets, show great improvement in the motion pattern recognition performance, importance of joint feature selection and fast incremental learning ability of the proposed framework.