A Discriminative Model with Multiple Temporal Scales for Action Prediction

By Yu Kong, Dmitry Kit, Yun Raymond Fu. 2014

164 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Skeleton-Based Online Action Prediction Using Scale Selection Network (2019, background): “tion recently [2], [3], [50], [51], [52], [53], [54].”
Deep Sequential Context Networks for Action Prediction (2017, methodology): “The proposed approach is significantly different from existing action prediction and early detection approaches [24, 13, 2, 14, 9, 19].”
A Review of Human Activity Recognition Methods (2015, background): “Predicting future events from partially unseen video clips with incomplete action execution has also been studied by Kong et al. (2014b).”
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction (2016, methodology): “Kong et al. [3] represented partial videos using bag-of-words features, and learned the multiple temporal scale support vector machine (MTSSVM) based on a structured SVM to recognize unfinished videos.”
Encouraging LSTMs to Anticipate Actions Very Early (2017, methodology): “This was followed by several attempts to model the dynamics of the observed actions, such as by introducing integral and dynamic bag-of-words [25], using spatial-temporal implicit shape models [45], extracting human body movements via skeleton information [47], and accounting for the complete and pa”
Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016, background): “More approaches can be found in [45] [52] [34] [3] [16] [33].”
Predicting the Where and What of Actors and Actions through Online Action Localization (2016, background): “There have been recent efforts to predict activities by early recognition [18, 16, 26, 14].”
Part-Activated Deep Reinforcement Learning for Action Prediction (2018, background): “But our PA-DRL outperformed the MTSSVM since OR = 0.4.”
Online Action Detection (2016, unknown): “Current work on related problems assumes simplified setups [2, 6, 7, 11, 12, 15].”
Activity Auto-Completion: Predicting Human Activities from Partial Videos (2015, methodology): “In recent years, predicting human activities from partially observed videos has been an active research topic [18, 8, 17, 13, 14].”
Progressive Teacher-Student Learning for Early Action Prediction (2019, background): “[23] developed a structured SVM formulation to capture the temporal evolution of human actions.”
Real-Time RGB-D Activity Prediction by Soft Regression (2016, methodology): “Recently, [7] extended the max-margin event detector for activity prediction and obtained the state-of-the-art results on several benchmark sets.”
Spatial, Structural and Temporal Feature Learning for Human Interaction Prediction (2016, methodology): “Kong et al. [13] represented partial videos using bag-of-words features, and learned the multiple temporal scale support vector machine (MTSSVM) based on structured SVM to recognize unfinished videos.”
SSNet: Scale Selection Network for Online 3D Action Prediction (2018, background): “Recognizing (predicting) an action before it is fully performed has attracted a lot of attention recently [31, 50, 4, 71, 36, 27, 28].”