Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments

Abstract: 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

• The paper introduces the multiple object forecasting (MOF) problem and presents the Citywalks dataset with over 200K high-resolution frames capturing pedestrian dynamics.
• It deploys a novel STED architecture that leverages optical flow and bounding box history to predict future object locations with enhanced accuracy.
• Experimental results demonstrate lower ADE/FDE and higher AIOU/FIOU, underscoring its potential for improving safety in autonomous navigation and surveillance applications.

Overview of "Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments"

The paper "Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments" by Styles, Guha, and Sanchez addresses a novel problem in the domain of computer vision known as Multiple Object Forecasting (MOF). The paper proposes a shift from the traditional trajectory prediction focus, which primarily revolves around birds-eye view perspectives, to an object-level approach predicting full bounding boxes over future frames. This transition is motivated by the need for enriched feature consideration such as appearance and motion details which are vital for tasks where having an overhead view is infeasible.

Key Contributions and Methods

The primary contribution of the paper is twofold: the introduction of the MOF problem and the development of the Citywalks dataset. This dataset comprises over 200,000 high-resolution video frames capturing pedestrian dynamics across diverse European environments, offering a robust platform for training and evaluation in MOF contexts.

The authors propose a novel STED (Spatio-Temporal Encoder-Decoder) architecture designed to address the challenges inherent to MOF. STED leverages both visual and temporal features via a combined utilization of optical flow and bounding box history, facilitating the prediction of future object locations despite variations in motion types and ego-motion complexities. Key experimental results demonstrate that STED outperforms existing adapted models in predicting future bounding boxes, substantiated by strong metrics achieved on both the Citywalks and MOT-17 datasets, indicating its robustness and generalization capabilities.

Experimental Highlights

Experimental evaluation highlights the superior performance of STED compared to several baselines, including Constant Velocity and Scale (CV-CS) models, Linear Kalman Filter (LKF), and modified trajectory prediction models such as FPL and DTP. STED achieves lower Average and Final Displacement Errors (ADE and FDE) as well as higher Average and Final Intersection-over-Union (AIOU and FIOU), underscoring its effectiveness in handling diverse prediction scenarios.

A significant aspect tested is the cross-dataset generalizability with other datasets like MOT-17, where STED's predictive accuracy remains high without additional fine-tuning. This evidences the potential for models trained on the Citywalks dataset to be applicable across different pedestrian forecasting challenges.

Implications and Future Directions

The introduction of MOF and the Citywalks dataset paves the way for exploring improved predictive strategies in scenarios where traditional methods may falter due to their reliance on constrained perspectives or inadequate feature capture. Future research could focus on enhancing model robustness across variants of real-world scenarios, exploring self-supervised learning techniques to overcome annotation challenges, and integrating multi-modal data to refine prediction accuracy further. Additionally, real-time performance gains through optimized architectures remain an exciting avenue.

Practically, the improvements in predictive tracking capabilities can be harnessed in domains such as autonomous vehicles and robotic navigation, where anticipating dynamic changes in pedestrian movements could lead to significant advancements in safety and efficiency.

In essence, this work sets a foundational stone for further inquiry and development explicitly aimed at robust real-world applications of advanced object location predictive systems within diverse and complex environments.