Trajectory Prediction Using Multivariate Time-series Data Stream Learning with Fused Kalman-filter and Evolving Correlated Horizons Feature Selection

Trajectory prediction of a moving object has imperative significance in both research and practical applications, ranging from target tracking, security surveillance and autonomous vehicle driving. For improving the efficacy of such prediction, a novel approach of data stream learning coupled with Kalman-filter and evolving correlated horizons feature selection (KF-ECH-FS) is proposed. KF has traditionally been used as a control-feedback-loop mechanism that corrects the errors from past trials, to predict the next-step position in trajectory prediction. In our fusion model here, KF and its windowed version are being used as predictor variables in a multivariate time series forecasting process. The predictor variables which serve as additional features aid in improving the trajectory prediction when only the relevant features are being selected in the incremental learning manner by multi-variate data stream analytics. Our proposed ECH-FS solves the problem of model overfitting when many features after expansion by time-series windowing are evaluated and selected along the learning process. A simple and efficient feature selection heuristics, Auto-encoder is used, along with data stream learning by Gate Recurrent Unit. The results, through an experimentation over a sample case of camera surveillance of accident prevention, show that our proposed KF-ECH-FS is superior to either KF or windowing alone in 1-step horizon trajectory prediction.