Learning-Deep-Learning

Social LSTM: Human Trajectory Prediction in Crowded Spaces

January 2020

tl;dr: Summary of the main idea.

Overall impression

Adds a social pooling layer that pools the hidden stages of the neighbors within a spatial radius.

Key ideas

• Instead of a spatial occupancy grid, replace the occupancy with LSTM embedding.

Technical details

• Social LSTM is actually done from a surveillance view point (between perspective onboard cameras and BEV).

Notes

• talk at CVPR: the animation of predicting a person passing through the gap of a crowd is cool.