# Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments

arXiv: https://arxiv.org/abs/1909.11944 · HTML: https://mlsanity.com/paper/1909.11944/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion](https://mlsanity.com/paper/2111.00993/) (2021)
- [Pedestrian 3D Bounding Box Prediction](https://mlsanity.com/paper/2206.14195/) (2022)
- [A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory](https://mlsanity.com/paper/2203.07098/) (2022)
- [Indoor Future Person Localization from an Egocentric Wearable Camera](https://mlsanity.com/paper/2103.04019/) (2021)
- [Multi-Camera Trajectory Forecasting with Trajectory Tensors](https://mlsanity.com/paper/2108.04694/) (2021)
- [Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting](https://mlsanity.com/paper/2108.10543/) (2021)
- [Joint Forecasting of Panoptic Segmentations with Difference Attention](https://mlsanity.com/paper/2204.07157/) (2022)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- [FDSG: Forecasting Dynamic Scene Graphs](https://mlsanity.com/paper/2506.01487/) (2025)
- [Future Object Detection with Spatiotemporal Transformers](https://mlsanity.com/paper/2204.10321/) (2022)
- [Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information](https://mlsanity.com/paper/2409.15224/) (2024)
- [Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering](https://mlsanity.com/paper/2603.18166/) (2026)

## Abstract (arXiv)

This paper introduces the problem of multiple object forecasting (MOF), in which the goal is to predict future bounding boxes of tracked objects. In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-level perspective and call for the prediction of full object bounding boxes, rather than trajectories alone. Towards solving this task, we introduce the Citywalks dataset, which consists of over 200k high-resolution video frames. Citywalks comprises of footage recorded in 21 cities from 10 European countries in a variety of weather conditions and over 3.5k unique pedestrian trajectories. For evaluation, we adapt existing trajectory forecasting methods for MOF and confirm cross-dataset generalizability on the MOT-17 dataset without fine-tuning. Finally, we present STED, a novel encoder-decoder architecture for MOF. STED combines visual and temporal features to model both object-motion and ego-motion, and outperforms existing approaches for MOF. Code & dataset link: https://github.com/olly-styles/Multiple-Object-Forecasting