Context-Aware Trajectory Prediction

Understand

Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might be perceived as points of attraction or obstacles.

• In this work, we present a new model for human trajectory prediction which is able to take advantage of both human-human and human-space interactions.
• The future trajectory of humans, are generated by observing their past positions and interactions with the surroundings.
• To this end, we propose a "context-aware" recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall.