How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting

@article{Monti2022HowMO,
  title={How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting},
  author={Alessio Monti and Angelo Porrello and Simone Calderara and Pasquale Coscia and Lamberto Ballan and Rita Cucchiara},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.04781}
}
Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a “history” of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g. up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect… 

References

SHOWING 1-10 OF 53 REFERENCES
Context-Aware Trajectory Prediction
TLDR
This work proposes 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, and evaluates the model on a public pedestrian datasets.
You'll never walk alone: Modeling social behavior for multi-target tracking
TLDR
A model of dynamic social behavior, inspired by models developed for crowd simulation, is introduced, trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera.
Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data
TLDR
Trajectron++ is a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data and outperforming a wide array of state-of-the-art deterministic and generative methods.
Group LSTM: Group Trajectory Prediction in Crowded Scenarios
TLDR
This work proposes a novel approach to predict future trajectories in crowded scenes, at the group level, by exploiting the motion coherency and cluster trajectories that have similar motion trends, so pedestrians within the same group can be well segmented.
Social LSTM: Human Trajectory Prediction in Crowded Spaces
TLDR
This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
TLDR
This collection was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California over a four-month period and forms the largest, most complete and detailed dataset to date for the development of self-driving, machine learning tasks such as motion forecasting, planning and simulation.
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
TLDR
This paper contributes a new large-scale dataset that collects videos of various types of targets that navigate in a real world outdoor environment such as a university campus and introduces a new characterization that describes the “social sensitivity” at which two targets interact.
What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction
TLDR
This work shows how neural networks for pedestrian motion prediction can be thoroughly evaluated and which research directions for neural motion prediction are promising in future and clarifies false assumptions about the problem itself.
Social Attention: Modeling Attention in Human Crowds
TLDR
This work proposes Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity, and demonstrates the performance against a state-of-the-art approach on two publicly available crowd datasets.
...
...