Vision-based human action recognition: An overview and real world challenges
Within a large range of applications in computer vision, Human Action Recognition has
become one of the most attractive research fields. Ambiguities in recognizing actions does …
become one of the most attractive research fields. Ambiguities in recognizing actions does …
Semantic human activity recognition: A literature review
M Ziaeefard, R Bergevin - Pattern Recognition, 2015 - Elsevier
This paper presents an overview of state-of-the-art methods in activity recognition using
semantic features. Unlike low-level features, semantic features describe inherent …
semantic features. Unlike low-level features, semantic features describe inherent …
Human action recognition and prediction: A survey
Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …
have been moving from inferring the present state to predicting the future state. Vision-based …
An end-to-end spatio-temporal attention model for human action recognition from skeleton data
Human action recognition is an important task in computer vision. Extracting discriminative
spatial and temporal features to model the spatial and temporal evolutions of different …
spatial and temporal features to model the spatial and temporal evolutions of different …
Actionvlad: Learning spatio-temporal aggregation for action classification
In this work, we introduce a new video representation for action classification that
aggregates local convolutional features across the entire spatio-temporal extent of the video …
aggregates local convolutional features across the entire spatio-temporal extent of the video …
Learning activity progression in lstms for activity detection and early detection
In this work we improve training of temporal deep models to better learn activity progression
for activity detection and early detection. Conventionally, when training a Recurrent Neural …
for activity detection and early detection. Conventionally, when training a Recurrent Neural …
Role-aware interaction generation from textual description
M Tanaka, K Fujiwara - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
This research tackles the problem of generating interaction between two human actors
corresponding to textual description. We claim that certain interactions, which we call …
corresponding to textual description. We claim that certain interactions, which we call …
Spatio-temporal attention-based LSTM networks for 3D action recognition and detection
Human action analytics has attracted a lot of attention for decades in computer vision. It is
important to extract discriminative spatio-temporal features to model the spatial and temporal …
important to extract discriminative spatio-temporal features to model the spatial and temporal …
Unsupervised learning from narrated instruction videos
We address the problem of automatically learning the main steps to complete a certain task,
such as changing a car tire, from a set of narrated instruction videos. The contributions of this …
such as changing a car tire, from a set of narrated instruction videos. The contributions of this …
The thumos challenge on action recognition for videos “in the wild”
Automatically recognizing and localizing wide ranges of human actions are crucial for video
understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve …
understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve …