  Trajectory Prediction of dynamic objects is a widely studied topic in the
field of artificial intelligence. Thanks to a large number of applications like
predicting abnormal events, navigation system for the blind, etc. there have
been many approaches to attempt learning patterns of motion directly from data
using a wide variety of techniques ranging from hand-crafted features to
sophisticated deep learning models for unsupervised feature learning. All these
approaches have been limited by problems like inefficient features in the case
of hand crafted features, large error propagation across the predicted
trajectory and no information of static artefacts around the dynamic moving
objects. We propose an end to end deep learning model to learn the motion
patterns of humans using different navigational modes directly from data using
the much popular sequence to sequence model coupled with a soft attention
mechanism. We also propose a novel approach to model the static artefacts in a
scene and using these to predict the dynamic trajectories. The proposed method,
tested on trajectories of pedestrians, consistently outperforms previously
proposed state of the art approaches on a variety of large scale data sets. We
also show how our architecture can be naturally extended to handle multiple
modes of movement (say pedestrians, skaters, bikers and buses) simultaneously.
