# Knowledge Transfer for Scene-specific Motion Prediction

arXiv: https://arxiv.org/abs/1603.06987 · HTML: https://mlsanity.com/paper/1603.06987/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 11 of 11)

- [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021)
- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction](https://mlsanity.com/paper/2204.11561/) (2022)
- [Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance](https://mlsanity.com/paper/2103.14231/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2302.07583/) (2023)
- [Distilling Knowledge for Short-to-Long Term Trajectory Prediction](https://mlsanity.com/paper/2305.08553/) (2023)
- [IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.15193/) (2023)
- [Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder](https://mlsanity.com/paper/2202.03954/) (2022)
- [Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?](https://mlsanity.com/paper/2106.15321/) (2021)
- [Unified Simulation, Perception, and Generation of Human Behavior](https://mlsanity.com/paper/2204.13678/) (2022)

## Abstract (arXiv)

When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements.