Context-Aware Trajectory Prediction (1705.02503v1)

Abstract: 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. We evaluate our model on a public pedestrian datasets, and we contribute a new challenging dataset that collects videos of humans that navigate in a (real) crowded space such as a big museum. Results show that our approach can predict human trajectories better when compared to previous state-of-the-art forecasting models.

• The paper presents a novel context-aware LSTM model that extends Social-LSTM by incorporating human-space interactions for enhanced trajectory forecasting.
• It employs a context-aware pooling mechanism alongside a new MuseumVisits dataset to capture complex dynamics in crowded environments.
• Experimental evaluations show significantly lower average displacement errors compared to existing methods, demonstrating the model's improved accuracy.

Context-Aware Trajectory Prediction in Crowded Spaces

The paper "Context-Aware Trajectory Prediction in Crowded Spaces" offers an advancement in the domain of human trajectory prediction by integrating considerations of both human-human and human-space interactions. This work is predicated on the understanding that human motion in crowded environments is influenced not only by interactions with other individuals but also by static elements within the environment. The authors propose a novel model that employs a context-aware recurrent neural network (specifically an LSTM architecture) to forecast human trajectories with improved accuracy in settings like sidewalks, museums, and shopping malls.

Core Contributions

1. Context-Aware LSTM Model: The principal innovation is a context-aware LSTM model that extends the Social-LSTM methodology by Alahi et al. to encapsulate human-space interactions alongside human-human dynamics. By modeling trajectory with an LSTM network, enriched by a "context-aware" pooling mechanism, the model adapts to both dynamic and static influences from the environment. This dual consideration allows for a finer prediction capability that accounts for not merely immediate human proximity but also spatial elements like artworks or exit points that might alter human behavior.
2. Dataset Contribution: The authors have introduced a new dataset, titled MuseumVisits, which captures trajectories within a real museum environment. The richness of this dataset lies in its depiction of complex interactions between individuals and the semantic atmosphere of the space, featuring diverse objects and cultural attractions that influence movement patterns. The availability of this dataset marks a significant resource for further studies and benchmarking trajectory prediction models.

Experimental Evaluation

The validity of the proposed model is demonstrated via experimental evaluations on both the new MuseumVisits dataset and the existing UCY dataset (focused on ZARA sequences). Results show a marked improvement in prediction accuracy over existing models such as vanilla LSTM and Social-LSTM, particularly within scenarios that involve intricate navigation around static elements. The proposed method achieves lower average displacement errors, exhibiting its strength in environments where a range of interactions is present.

Implications and Future Research Directions

The implications of this research are manifold, spanning theoretical augmentation of trajectory prediction models and practical applications in robotics, autonomous navigation, and crowd management systems. By integrating environmental context into trajectory prediction, models can better serve in real-time applications like collision avoidance systems in vehicular and pedestrian scenarios.

For future development, researchers could explore enhancements in modeling static space interactions, potentially incorporating more sophisticated context-aware mechanisms or examining deeper semantic understanding of spatial elements. Moreover, the extension of this model to predictive tasks in even more dynamic urban environments could be beneficial. The prospect of using such models alongside sensor-fusion techniques for real-time systems remains a fertile ground for investigation.

The paper's methodology and associated dataset pave the way for enriching trajectory prediction models with context, opening avenues for increasingly nuanced and accurate applications within AI-driven navigation and intelligent space design.