# Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior

arXiv: https://arxiv.org/abs/2006.04700 · HTML: https://mlsanity.com/paper/2006.04700/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [Stepwise Goal-Driven Networks for Trajectory Prediction](https://mlsanity.com/paper/2103.14107/) (2021)
- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning](https://mlsanity.com/paper/2210.07886/) (2022)
- [Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion](https://mlsanity.com/paper/2111.00993/) (2021)
- [A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.10424/) (2023)
- [NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting](https://mlsanity.com/paper/2102.05624/) (2021)
- [Indoor Future Person Localization from an Egocentric Wearable Camera](https://mlsanity.com/paper/2103.04019/) (2021)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements](https://mlsanity.com/paper/2111.05060/) (2021)
- [Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network](https://mlsanity.com/paper/2112.06624/) (2021)
- [Frozen Forecasting: A Unified Evaluation](https://mlsanity.com/paper/2507.13942/) (2025)
- [Leveraging Gaze and Set-of-Mark in VLLMs for Human-Object Interaction Anticipation from Egocentric Videos](https://mlsanity.com/paper/2604.03667/) (2026)
- ["If you could see me through my eyes": Predicting Pedestrian Perception](https://mlsanity.com/paper/2202.13981/) (2022)
- [Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos](https://mlsanity.com/paper/2308.06628/) (2023)
- [BlindSpotNet: Seeing Where We Cannot See](https://mlsanity.com/paper/2207.03870/) (2022)
- [A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2406.18050/) (2024)
- [Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information](https://mlsanity.com/paper/2409.15224/) (2024)

## Abstract (arXiv)

In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle. We approach two fundamental challenges: (1) the partial visibility due to the egocentric view with a single RGB camera and considerable field-of-view change due to the egomotion of the vehicle; (2) the multimodality of the distribution of future states. In contrast to many previous works, we do not assume structural knowledge from maps. We rather estimate a reachability prior for certain classes of objects from the semantic map of the present image and propagate it into the future using the planned egomotion. Experiments show that the reachability prior combined with multi-hypotheses learning improves multimodal prediction of the future location of tracked objects and, for the first time, the emergence of new objects. We also demonstrate promising zero-shot transfer to unseen datasets. Source code is available at hrefhttps://github.com/lmb-freiburg/FLN-EPN-RPNthis https URL.