Giter Club home page Giter Club logo

awesome-activity-prediction's Introduction

Awesome Activity Prediction: Awesome

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

  • 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.
  • 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

  • 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

  • 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

  • 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

CC0

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

awesome-activity-prediction's People

Contributors

chinancheng avatar ching-ju-henry avatar

Stargazers

 avatar

Watchers

 avatar guoshuangshuang avatar paper2code - bot avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.