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Action-based Contrastive Learning for Trajectory Prediction
Computer Vision and Pattern Recognition · Submitted 18 Jul 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Thus, integrating the proposed contrastive framework within a trajectory prediction model improves its results and outperforms state-of-the-art methods on three trajectory prediction benchmarks [31, 32, 26].
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The abstract, sentence by sentence
Trajectory prediction is an essential task for successful human robot interaction, such as in autonomous driving. In this work, we address the problem of predicting future pedestrian trajectories in a first person view setting with a moving camera. To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings. The fundamental idea behind this new loss is that trajectories of pedestrians performing the same action should be closer to each other in the feature space than the trajectories of pedestrians with significantly different actions. In other words, we argue that behavioral information about pedestrian action influences their future trajectory. Furthermore, we introduce a novel sampling strategy for trajectories that is able to effectively increase negative and positive contrastive samples. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian behavior, i.e. action, effectively, and it learns a better trajectory representation. Thus, integrating the proposed contrastive framework within a trajectory prediction model improves its results and outperforms state-of-the-art methods on three trajectory prediction benchmarks [31, 32, 26].
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Trajectory prediction is an essential task for successful human robot interaction, such as in autonomous driving. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | In this work, we address the problem of predicting future pedestrian trajectories in a first person view setting with a moving camera. | background 0.55 | 0.46 | 0.02 | asserted 0.79 |
| s2 | To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings. | contribution 0.83 | 0.91 | 0.07 | asserted 0.98 |
| s3 | The fundamental idea behind this new loss is that trajectories of pedestrians performing the same action should be closer to each other in the feature space than the trajectories of pedestrians with significantly different actions. | method 0.83 | 0.35 | 0.08 | asserted 0.99 |
| s4 | In other words, we argue that behavioral information about pedestrian action influences their future trajectory. | interpretation 0.58 | 0.27 | 0.10 | asserted 0.69 |
| s5 | Furthermore, we introduce a novel sampling strategy for trajectories that is able to effectively increase negative and positive contrastive samples. | method 0.60 | 0.91 | 0.03 | asserted 0.99 |
| s6 | Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. | method 0.93 | 0.53 | 0.03 | asserted 0.99 |
| s7 | Results show that our proposed contrastive framework employs contextual information about pedestrian behavior, i.e. action, effectively, and it learns a better trajectory representation. | result 0.76 | 0.94 | 0.28 | asserted 1.00 |
| s8 | Thus, integrating the proposed contrastive framework within a trajectory prediction model improves its results and outperforms state-of-the-art methods on three trajectory prediction benchmarks [31, 32, 26]. | result 1.00 | 0.94 | 0.88 | asserted 1.00 |
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The paper
Original abstract
Trajectory prediction is an essential task for successful human robot interaction, such as in autonomous driving. In this work, we address the problem of predicting future pedestrian trajectories in a first person view setting with a moving camera. To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings. The fundamental idea behind this new loss is that trajectories of pedestrians performing the same action should be closer to each other in the feature space than the trajectories of pedestrians with significantly different actions. In other words, we argue that behavioral information about pedestrian action influences their future trajectory. Furthermore, we introduce a novel sampling strategy for trajectories that is able to effectively increase negative and positive contrastive samples. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian behavior, i.e. action, effectively, and it learns a better trajectory representation. Thus, integrating the proposed contrastive framework within a trajectory prediction model improves its results and outperforms state-of-the-art methods on three trajectory prediction benchmarks [31, 32, 26].
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We train the model on all datasets with Adam optimizer [ref] using a batch size of 128 and a learning rate of 0.001.
Adam: A Method for Stochastic Optimization · Implementation details. · 0.99
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