Social Attention: Modeling Attention in Human Crowds

Vemula, Anirudh; Muelling, Katharina; Oh, Jean

doi:10.1109/icra.2018.8460504

Cited by 605 publications

(499 citation statements)

References 35 publications

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“…Following [11], [13], [14], we compare these methods in terms of the Average Displacement Error (ADE) and Final Displacement Error (FDE). The ADE is defined as the mean Euclidean distance between predicted coordinates and the ground truth.…”

Section: Methodsmentioning

confidence: 99%

StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology

Zhu¹,

Qian²,

Ren³

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Pedestrian trajectory prediction is crucial for many important applications. This problem is a great challenge because of complicated interactions among pedestrians. Previous methods model only the pairwise interactions between pedestrians, which not only oversimplifies the interactions among pedestrians but also is computationally inefficient. In this paper, we propose a novel model StarNet to deal with these issues. StarNet has a star topology which includes a unique hub network and multiple host networks. The hub network takes observed trajectories of all pedestrians to produce a comprehensive description of the interpersonal interactions. Then the host networks, each of which corresponds to one pedestrian, consult the description and predict future trajectories. The star topology gives StarNet two advantages over conventional models. First, StarNet is able to consider the collective influence among all pedestrians in the hub network, making more accurate predictions. Second, StarNet is computationally efficient since the number of host network is linear to the number of pedestrians. Experiments on multiple public datasets demonstrate that StarNet outperforms multiple state-of-the-arts by a large margin in terms of both accuracy and efficiency.

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Section: Methodsmentioning

confidence: 99%

StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology

Zhu¹,

Qian²,

Ren³

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Many approaches jointly model the past motion of multiple agents in the scene to capture interaction between agents [5], [15], [12], [10], [7], [11]. This is typically done by pooling the RNN states of individual agents in a social tensor [5], [12], [11], using graph neural networks [16] or by modeling pairwise distances between agents along with max pooling [8], [10], [7].…”

Section: Related Studiesmentioning

confidence: 99%

Scene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids

Ridel

Deo

Wolf

et al. 2020

IEEE Robot. Autom. Lett.

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Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories. The underlying scene and past motion of agents can provide useful cues to predict their future motion. However, the heterogeneity of the two inputs poses a challenge for learning a joint representation of the scene and past trajectories. To address this challenge, we propose a model based on grid representations to forecast agent trajectories. We represent the past trajectories of agents using binary 2-D grids, and the underlying scene as a RGB birds-eye view (BEV) image, with an agent-centric frame of reference. We encode the scene and past trajectories using convolutional layers and generate trajectory forecasts using a Convolutional LSTM (ConvLSTM) decoder. Results on the publicly available Stanford Drone Dataset (SDD) show that our model outperforms prior approaches and outputs realistic future trajectories that comply with scene structure and past motion.

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“…The model captures different styles of navigation but does not make any differences between structured and unstructured environments. [21] handles prediction using a spatio-temporal graph which models both position evolution and interaction between pedestrians. [9] embodies vislet information within the social-pooling mechanism also relying on mutual faces orientation to augment space perception.…”

Section: Related Workmentioning

confidence: 99%

Social and Scene-Aware Trajectory Prediction in Crowded Spaces

Lisotto

Coscia

Ballan

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Mimicking human ability to forecast future positions or interpret complex interactions in urban scenarios, such as streets, shopping malls or squares, is essential to develop socially compliant robots or self-driving cars. Autonomous systems may gain advantage on anticipating human motion to avoid collisions or to naturally behave alongside people. To foresee plausible trajectories, we construct an LSTM (long short-term memory)-based model considering three fundamental factors: people interactions, past observations in terms of previously crossed areas and semantics of surrounding space. Our model encompasses several pooling mechanisms to join the above elements defining multiple tensors, namely social, navigation and semantic tensors. The network is tested in unstructured environments where complex paths emerge according to both internal (intentions) and external (other people, not accessible areas) motivations. As demonstrated, modeling paths unaware of social interactions or context information, is insufficient to correctly predict future positions. Experimental results corroborate the effectiveness of the proposed framework in comparison to LSTM-based models for human path prediction.

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Social Attention: Modeling Attention in Human Crowds

Cited by 605 publications

References 35 publications

StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology

StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology

Scene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids

Social and Scene-Aware Trajectory Prediction in Crowded Spaces

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