SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction

@article{Syed2019SSegLSTMSS,
  title={SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction},
  author={Arsal Syed and Brendan Tran Morris},
  journal={2019 IEEE Intelligent Vehicles Symposium (IV)},
  year={2019},
  pages={2504-2509}
}
  • Arsal Syed, B. Morris
  • Published 9 June 2019
  • Computer Science
  • 2019 IEEE Intelligent Vehicles Symposium (IV)
In this paper, we propose the use of semantic segmentation to incorporate scene information for better understanding of human motion in crowded environments. Our proposed SSeg-LSTM method leverages SegNet, which is a semantic segmentation encoder-decoder architecture, to extract semantically meaningful scene features. We then train the Social Scene LSTM (SS-LSTM) model with the contextual information regarding dynamics, social neighborhood, and scene semantics to predict future trajectory… 
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References

SHOWING 1-10 OF 16 REFERENCES
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction
TLDR
A novel hierarchical LSTM-based network is proposed to consider both the influence of social neighbourhood and scene layouts in pedestrian trajectory prediction, showing that the method outperforms other methods and that using circular shape neighbourhood improves the prediction accuracy.
Social LSTM: Human Trajectory Prediction in Crowded Spaces
TLDR
This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
Convolutional Social Pooling for Vehicle Trajectory Prediction
  • Nachiket Deo, M. Trivedi
  • Computer Science
    2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2018
TLDR
This paper proposes an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social Pooling layers for robustly learning interdependencies in vehicle motion and outputs a multi-modal predictive distribution over future trajectories based on maneuver classes.
Context-Aware Trajectory Prediction
TLDR
This work proposes a “context-aware” recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall, and evaluates the model on a public pedestrian datasets.
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
TLDR
It is shown that a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve sophisticated results compared to elaborated models in such scenarios and some recommendations for overcoming demonstrated shortcomings are given.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
TLDR
The results show that SegNet achieves state-of-the-art performance even without use of additional cues such as depth, video frames or post-processing with CRF models.
Semantic object classes in video: A high-definition ground truth database
TLDR
The Cambridge-driving Labeled Video Database (CamVid) is presented as the first collection of videos with object class semantic labels, complete with metadata, and the relevance of the database is evaluated by measuring the performance of an algorithm from each of three distinct domains: multi-class object recognition, pedestrian detection, and label propagation.
Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network
TLDR
The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles.
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
TLDR
SoPhie is presented; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene.
You'll never walk alone: Modeling social behavior for multi-target tracking
TLDR
A model of dynamic social behavior, inspired by models developed for crowd simulation, is introduced, trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera.
...
1
2
...