Context-Based Pedestrian Path Prediction

Kooij, Julian F. P.; Schneider, Nicolas; Flohr, Fabian; Gavrila, Dariu M.

doi:10.1007/978-3-319-10599-4_40

Cited by 213 publications

(194 citation statements)

References 27 publications

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“…A large body of work learns motion patterns through clustering trajectories [26,30,46,77]. More approaches can be found in [45,52,34,3,16,33]. Kitani et.…”

Section: Related Workmentioning

confidence: 99%

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Social LSTM: Human Trajectory Prediction in Crowded Spaces

Alahi

Goel

Ramanathan

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2,736

2,594

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Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets . We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.

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“…A large body of work learns motion patterns through clustering trajectories [26,30,46,77]. More approaches can be found in [45,52,34,3,16,33]. Kitani et.…”

Section: Related Workmentioning

confidence: 99%

“…Other approaches like [27,19,42,36] showed the use of scene semantics to predict goals and paths for human navigation. Scene semantics has also been used to predict multiple object dynamics [17,36,34,28]. These works are mostly restricted to the use of static scene information to predict human motion or activity.…”

Section: Related Workmentioning

confidence: 99%

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Alahi

Goel

Ramanathan

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2,736

2,594

View full text Add to dashboard Cite

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“…As in [3], [14], observations are filtered online using a recursive Bayesian filter with the selected motion model. At any frame, a predictive distribution for future positions is obtained by executing a filter's 'predict' step several times without any 'update'.…”

Section: Methodsmentioning

confidence: 99%

“…A common approach leverages the motion models which are already an integral part of the tracker's filter [3]. More informed predictions are obtained by conditioning dynamics on additional cues, such as intent and awareness of the pedestrian [14], [15] or driver [16]. Others use the dynamics of the appearance (e.g.…”

Section: Related Workmentioning

confidence: 99%

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Using road topology to improve cyclist path prediction

Pool

Kooij

Gavrila

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then spatially aligned to local curves and crossings in the road. We study a standard approach for path prediction in the literature based on Kalman Filters, as well as a mixture of specialized filters related to specific road orientations at junctions. Our experiments demonstrate an improved prediction accuracy (up to 20% on sharp turns) of mixing specialized motion models for canonical directions, and prior knowledge on the road topology. The new track data complements the existing video, disparity and annotation data of the original benchmark, and will be made publicly available.

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Action Anticipation with RBF Kernelized Feature Mapping RNN

Shi

Fernando

Hartley

2018

Lecture Notes in Computer Science

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We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN . Our novel RNN architecture builds upon three effective principles of machine learning, namely parameter sharing, Radial Basis Function kernels and adversarial training. Using only some of the earliest frames of a video, the feature mapping RNN is able to generate future features with a fraction of the parameters needed in traditional RNN. By feeding these future features into a simple multilayer perceptron facilitated with an RBF kernel layer, we are able to accurately predict the action in the video.In our experiments, we obtain 18% improvement on JHMDB-21 dataset, 6% on UCF101-24 and 13% improvement on UT-Interaction datasets over prior stateof-the-art for action anticipation.

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Context-Based Pedestrian Path Prediction

Cited by 213 publications

References 27 publications

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Using road topology to improve cyclist path prediction

Action Anticipation with RBF Kernelized Feature Mapping RNN

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