Name
Abstract | ||
|---|---|---|
Predicting the walking path of a pedestrian in crowds is a pivotal step towards understanding his/her behavior. This is one of the recently emerging tasks in computer vision scarcely addressed to date. In this paper, we put forth a deep spatio-temporal learning-forecasting approach, which is composed of two modules. First, displacement information from pedestrians' walking history is extracted and fed into a convolutional layer in order to learn the undergoing motion patterns and produce high-level representations. Second, unlike the mainstream literature which learns the temporal or the spatial dynamics among the pedestrians separately, we propose to embed both components into a single framework via a Long-Short Term Memory based architecture that takes as input the previously extracted high-level motion cues and outputs the potential future walking routes of all pedestrians in one shot. We evaluate our approach on three large benchmark datasets, and show that it introduces large margin improvements with respect to recent works in the literature, both in short and long-term forecasting scenarios.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3123266.3123287 | MM '17: ACM Multimedia Conference
Mountain View
California
USA
October, 2017 |
Keywords | Field | DocType |
Path forecasting, deep learning, crowd motion dynamics, computer vision | Computer vision,Motion cues,Crowds,Architecture,Pedestrian,Computer science,Artificial intelligence,Deep learning,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-4906-2 | 2 | 0.39 |
References | Authors | |
19 | 1 | |