10,134 research outputs found
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video
understanding. Having insight into the movement of pedestrians is crucial for
ensuring safe operation in a variety of applications including autonomous
vehicles, social robots, and environmental monitoring. Current works in this
area utilize complex generative or recurrent methods to capture many possible
futures. However, despite the inherent real-time nature of predicting future
paths, little work has been done to explore accurate and computationally
efficient approaches for this task. To this end, we propose a convolutional
approach for real-time pedestrian path prediction, CARPe. It utilizes a
variation of Graph Isomorphism Networks in combination with an agile
convolutional neural network design to form a fast and accurate path prediction
approach. Notable results in both inference speed and prediction accuracy are
achieved, improving FPS considerably in comparison to current state-of-the-art
methods while delivering competitive accuracy on well-known path prediction
datasets.Comment: AAAI-21 Camera Read
Transferable Pedestrian Motion Prediction Models at Intersections
One desirable capability of autonomous cars is to accurately predict the
pedestrian motion near intersections for safe and efficient trajectory
planning. We are interested in developing transfer learning algorithms that can
be trained on the pedestrian trajectories collected at one intersection and yet
still provide accurate predictions of the trajectories at another, previously
unseen intersection. We first discussed the feature selection for transferable
pedestrian motion models in general. Following this discussion, we developed
one transferable pedestrian motion prediction algorithm based on Inverse
Reinforcement Learning (IRL) that infers pedestrian intentions and predicts
future trajectories based on observed trajectory. We evaluated our algorithm on
a dataset collected at two intersections, trained at one intersection and
tested at the other intersection. We used the accuracy of augmented
semi-nonnegative sparse coding (ASNSC), trained and tested at the same
intersection as a baseline. The result shows that the proposed algorithm
improves the baseline accuracy by 40% in the non-transfer task, and 16% in the
transfer task
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