TrajSense: Trajectory Prediction from Sparse and Missing External Sensor Data

Abstract

In this demonstration, we present a framework to predict the movement of moving objects under the circumstance where external sensors placed on the road-sides (e.g., traffic surveillance cameras) capture their trajectories. The reported positions in such trajectories are sparse due to the sparsity of the sensor distribution, and incomplete, since the sensors may fail to register the passage of objects. In our framework, we cope with the missing data coming from the external sensor trajectories, which improves the quality of predictions in terms of accuracy and closeness in the road network.