# CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces

arXiv: https://arxiv.org/abs/2202.08506 · HTML: https://mlsanity.com/paper/2202.08506/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2005.08514/) (2020)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [Multi-Agent Tensor Fusion for Contextual Trajectory Prediction](https://mlsanity.com/paper/1904.04776/) (2019)
- [Peeking into the Future: Predicting Future Person Activities and Locations in Videos](https://mlsanity.com/paper/1902.03748/) (2019)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction](https://mlsanity.com/paper/1903.02793/) (2019)
- [The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction](https://mlsanity.com/paper/1912.06445/) (2019)
- [Recursive Social Behavior Graph for Trajectory Prediction](https://mlsanity.com/paper/2004.10402/) (2020)
- [How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction](https://mlsanity.com/paper/1801.06523/) (2018)
- [CAR-Net: Clairvoyant Attentive Recurrent Network](https://mlsanity.com/paper/1711.10061/) (2017)
- [Pedestrian Trajectory Prediction with Convolutional Neural Networks](https://mlsanity.com/paper/2010.05796/) (2020)
- [Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision](https://mlsanity.com/paper/2012.01884/) (2020)

## Built on by (most cited first, 18 of 18)

- [Human Trajectory Prediction via Neural Social Physics](https://mlsanity.com/paper/2207.10435/) (2022)
- [SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.05370/) (2023)
- [Domain Re-Modulation for Few-Shot Generative Domain Adaptation](https://mlsanity.com/paper/2302.02550/) (2023)
- [Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums](https://mlsanity.com/paper/2304.05106/) (2023)
- [Under the Hood of Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2203.11878/) (2022)
- [Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights](https://mlsanity.com/paper/2501.07711/) (2025)
- [Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios](https://mlsanity.com/paper/2306.08386/) (2023)
- [SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2409.14984/) (2024)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2504.15616/) (2025)
- [Sparse Pedestrian Character Learning for Trajectory Prediction](https://mlsanity.com/paper/2311.15512/) (2023)
- [TrajPRed: Trajectory Prediction with Region-based Relation Learning](https://mlsanity.com/paper/2404.06971/) (2024)
- [Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles](https://mlsanity.com/paper/2409.08665/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Learning Extremely High Density Crowds as Active Matters](https://mlsanity.com/paper/2503.12168/) (2025)
- [Reverberation: Learning the Latencies Before Forecasting Trajectories](https://mlsanity.com/paper/2511.11164/) (2025)
- [Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction](https://mlsanity.com/paper/2307.14788/) (2023)
- [EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan](https://mlsanity.com/paper/2609.06195/) (2026)

## Abstract (arXiv)

Trajectory prediction aims to predict the movement trend of the agents like pedestrians, bikers, vehicles. It is helpful to analyze and understand human activities in crowded spaces and widely applied in many areas such as surveillance video analysis and autonomous driving systems. Thanks to the success of deep learning, trajectory prediction has made significant progress. The current methods are dedicated to studying the agents' future trajectories under the social interaction and the sceneries' physical constraints. Moreover, how to deal with these factors still catches researchers' attention. However, they ignore the Semantic Shift Phenomenon when modeling these interactions in various prediction sceneries. There exist several kinds of semantic deviations inner or between social and physical interactions, which we call the "Gap". In this paper, we propose a Contextual Semantic Consistency Network (CSCNet) to predict agents' future activities with powerful and efficient context constraints. We utilize a well-designed context-aware transfer to obtain the intermediate representations from the scene images and trajectories. Then we eliminate the differences between social and physical interactions by aligning activity semantics and scene semantics to cross the Gap. Experiments demonstrate that CSCNet performs better than most of the current methods quantitatively and qualitatively.