Awesome Activity Prediction:

A paper list of Activity Prediction and related area resources, inspired by awesome-computer-vision and awesome-action-recognition.

Contents

• Action Prediction In Early Stage
• Activity Prediction
• Event Prediction
• Human Trajectory Prediction

Action Prediction In Early Stage

• Action Anticipation with RBF Kernelized Feature Mapping RNN [Paper]
  • Y. Shi, B. Fernando, R. Hartley, ECCV 2018.
• Part-Activated Deep Reinforcement Learning for Action Prediction [Paper]
  • L. Chen, J. Lu, Z. Song, J. Zhou, ECCV 2018.
• Temporal Relational Reasoning in Videos [Paper] [Project] [Code]
  • B. Zhou, A. Andonian, A. Oliva, A. Torralba, ECCV 2018.
• Human Action Recognition and Prediction: A Survey [Paper]
  • Y. Kong, Y. Fu, arxiv 2018.
• SSNet: Scale Selection Network for Online 3D Action Prediction [Paper]
  • J. Liu, A. Shahroudy, G. Wang, L. Y. Duan, A. C. Kot, CVPR 2018.
• Action Prediction from Videos via Memorizing Hard-to-Predict Samples [Paper]
  • Y. Kong, S. Gao, B. Sun, Y. Fu, AAAI 2018.
• On Encoding Temporal Evolution for Real-time Action Prediction [Paper]
  • F. Rezazadegan, S.Shirazi, M. Baktashmotlagh, L. S. Davis, arXiv 2018.
• Predictive Learning: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction [Paper]    
  • Y. Runsheng, S. Zhenyu, M. Qiongxiong, Q. Laiyun, arXiv 2017.
• Encouraging LSTMs to Anticipate Actions Very Early [Paper]
  • M. S. Aliakbarian, F. Saleh, M. Salzmann, B. Fernando, L. Petersson, L. Andersson, ICCV 2017.
• Online Real-time Multiple Spatiotemporal Action Localisation and Prediction [Paper] [Code]
  • G. Singh, S. Saha, M. Sapienza, P. Torr, F. Cuzzolin, ICCV 2017.
• Visual Forecasting by Imitating Dynamics in Natural Sequences [Paper]
  • K. H. Zeng, W. B. Shen, D. A. Huang, M. Sun, J. C. Niebles, ICCV 2017.
• Binary Coding for Partial Action Analysis with Limited Observation Ratios [Paper]
  • J. Qin, L. Liu, L. Shao, B. Ni, C. Chen, F. Shen, Y. Wang, CVPR 2017.
• Deep Sequential Context Networks for Action Prediction [Paper]
  • Y. Kong, Z. Tao, Y. Fu, CVPR 2017.
• RED: Reinforced Encoder-Decoder Networks for Action Anticipation [Paper]
  • J. Gao, Z. Yang, R. Nevatia, BMVC 2017.
• Anticipating Visual Representations from Unlabeled Video [Paper]
  • C. Vondrick, H. Pirsiavash, A. Torralba, CVPR 2016.
• Learning Activity Progression in LSTMs for Activity Detection and Early Detection [Paper]
  • S. Ma, L. Sigal, S. Sclaroff, CVPR 2016.
• Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation [Paper]
  • M. S. Aliakbarian, F. Saleh, B. Fernando, M. Salzmann, L. Petersson, L. Andersson, arxiv 2016.
• A hierarchical representation for future action prediction [Paper]
  • T. Lan, T. C. Chen, and S. Savarese, ECCV 2014.
• A Discriminative Model with Multiple Temporal Scales for Action Prediction [Paper]
  • Y. Kong, D. Kit, Y. Fu, ECCV 2014.
• Human activity prediction: Early recognition of ongoing activities from streaming videos [Paper]
  • M. S. Ryoo, ICCV 2011.

Activity Prediction

• Egocentric Activity Prediction via Event Modulated Attention [Paper]
  • Y. Shen*, B. Ni*, Z. Li, N. Zhuang, ECCV 2018.
• When will you do what? - Anticipating Temporal Occurrences of Activities [Paper] [Code]
  • Y. A. Farha, A. Richard, J. Gall, CVPR 2018.
• First-Person Activity Forecasting with Online Inverse Reinforcement Learning [Paper] [Project]
  • N. Rhinehart, K. M. Kitani, ICCV 2017.
• Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos [Paper]
  • T. Mahmud, M. Hasan, A. K. Roy-Chowdhury, ICCV 2017.
• Anticipating Daily Intention using On-Wrist Motion Triggered Sensing [Paper] [Project]
  • T. Y. Wu*, T. A. Chien*, C. S. Chan, C. W. Hu, M. Sun, ICCV 2017.
• Predicting Human Activities Using Stochastic Grammar [Paper] [Code]
  • S. Qi, S. Huang, P. Wei, S. C. Zhu, ICCV 2017.
• Using Cross-Model EgoSupervision to Learn Cooperative Basketball Intention [Paper]
  • G. Bertasius, J. Shi, ICCV 2017 Workshop.
• Long-Term Activity Forecasting using First-Person Vision [Paper]
  • S. Z. Bokhari, K. M. Kitani, ACCV 2016.
• Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture [Paper]
  • A. Jain, A. Singh, H. S. Koppula, S. Soh, A. Saxena, ICRA 2016.

Event Prediction

• Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB [Paper]
  • T. Suzuki, H. Kataoka, Y. Aoki, Y. Satoh, CVPR 2018.
• Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization [Paper]
  • K. H. Zeng, S. H. Chou, F. H. Chan, J. C. Niebles, M. Sun, CVPR 2017.
• Anticipating accidents in dashcam videos [Paper] [Code] [Project]
  • F. H. Chan, Y. T. Chen, Y. Xiang, M. Sun, ACCV 2016.
• Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models [Paper]
  • A. Jain, H. S. Koppula, B. Raghavan, S. Soh, A. Saxena, ICCV 2015.

Human Trajectory Prediction

• Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty[Paper]
  • A. Bhattacharyya, M. Fritz, B. Schiele, CVPR 2018.
• Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction[Paper] [Code]
  • Y. Xu, Z. Piao, S. Gao, CVPR 2018.
• Human Trajectory Prediction using Spatially aware Deep Attention Models [Paper]
  • D. Varshneya, G. Srinivasaraghavan, arxiv 2017.
• Context-Aware Trajectory Prediction [Paper]
  • F. Bartoli, G. Lisanti, L. Ballan, A. D. Bimbo, arxiv 2017.
• Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection [Paper]
  • T. Fernando, S. Denman, S. Sridharan, C. Fookes, arxiv 2017.
• Forecasting Interactive Dynamics of Pedestrians with Fictitious Play [Paper]
  • W. C. Ma, D. A. Huang, N. Lee, K. M. Kitani, CVPR 2017.
• Social LSTM: Human Trajectory Prediction in Crowded Spaces [Paper]
  • A. Alahi∗, K. Goel*, V. Ramanathan, A. Robicquet, Li Fei-Fei, S. Savarese, CVPR 2016.

Contributing

Feel free to send me email or Pull Request to add links.

Licenses

To the extent possible under law, Chin-An Cheng, Ching-Ju Cheng has waived all copyright and related or neighboring rights to this work.