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Pedestrian 3D Bounding Box Prediction

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arxiv 2206.14195 v1 pith:PK5J7IGY submitted 2022-06-28 cs.CV cs.RO

Saeed Saadatnejad , Yi Zhou Ju , Alexandre Alahi This is my paper · ORCID
classification cs.CVcs.RO
keywords predictionboundingautonomoushumanpedestrianspredictreal-worldaction
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Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained predictions (human body keypoints prediction), we focus on 3D bounding boxes, which are reasonable estimates of humans without modeling complex motion details for autonomous vehicles. This gives the flexibility to predict in longer horizons in real-world settings. We suggest this new problem and present a simple yet effective model for pedestrians' 3D bounding box prediction. This method follows an encoder-decoder architecture based on recurrent neural networks, and our experiments show its effectiveness in both the synthetic (JTA) and real-world (NuScenes) datasets. The learned representation has useful information to enhance the performance of other tasks, such as action anticipation. Our code is available online: https://github.com/vita-epfl/bounding-box-prediction

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

    cs.CV 2025-07 conditional novelty 6.0 of 10

    An attention-based pose encoder improves trajectory prediction across LSTM, GAN, MLP, and Transformer models on several datasets, though a capacity confound weakens the attribution.

  2. Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A trajectory prediction model that decomposes forecasts into a linear base, a self-sourced vibration, and a social resonance vibration, achieving strong benchmark results with an interpretable decomposition.

  3. Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction

    cs.CV 2024-12 conditional novelty 4.0 of 10

    A trajectory prediction model that adds hand-crafted group detection and field-of-view based social perception features improves pedestrian forecasting accuracy on some benchmarks.

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