Spatio-Temporal Action Instance Segmentation and Localisation

2020 ◽  
pp. 141-161
Author(s):  
Suman Saha ◽  
Gurkirt Singh ◽  
Michael Sapienza ◽  
Philip H. S. Torr ◽  
Fabio Cuzzolin
Keyword(s):  
Spatio Temporal ◽  
Temporal Action ◽  
Instance Segmentation

Spatio-Temporal Action Localization for Pedestrian Action Detection

2020 ◽  
pp. 1337-1341
Author(s):  
Linchao He ◽  
Jiong Mu ◽  
Mengting Luo ◽  
Yunlu Lu ◽  
Xuefeng Tan ◽  
...  
Keyword(s):  
Action Detection ◽  
Action Localization ◽  
Spatio Temporal ◽  
Temporal Action

scholarly journals Discovering spatio-temporal action tubes

2019 ◽  
Vol 58 ◽  
pp. 515-524 ◽  
Author(s):  
Yuancheng Ye ◽  
Xiaodong Yang ◽  
YingLi Tian
Keyword(s):  
Spatio Temporal ◽  
Temporal Action

EPV 19. Dissociable regions for recognition and execution of conceptual and spatio-temporal action characteristics in acute stroke patients

2016 ◽  
Vol 127 (9) ◽  
pp. e221-e223
Author(s):  
M. Martin ◽  
A. Dressing ◽  
L. Beume ◽  
T. Bormann ◽  
I. Mader ◽  
...  
Keyword(s):  
Acute Stroke ◽  
Stroke Patients ◽  
Spatio Temporal ◽  
Temporal Action

scholarly journals STEm-Seg: Spatio-Temporal Embeddings for Instance Segmentation in Videos

2020 ◽  
pp. 158-177
Author(s):  
Ali Athar ◽  
Sabarinath Mahadevan ◽  
Aljos̆a Os̆ep ◽  
Laura Leal-Taixé ◽  
Bastian Leibe
Keyword(s):  
Spatio Temporal ◽  
Instance Segmentation

scholarly journals Finding Action Tubes with a Sparse-to-Dense Framework

2020 ◽  
Vol 34 (07) ◽  
pp. 11466-11473
Author(s):  
Yuxi Li ◽  
Weiyao Lin ◽  
Tao Wang ◽  
John See ◽  
Rui Qian ◽  
...  
Keyword(s):  
Dynamic Feature ◽  
Video Streams ◽  
Short Term ◽  
Key Characteristics ◽  
Benchmark Datasets ◽  
Tube Output ◽  
Spatio Temporal ◽  
First Time ◽  
Temporal Action

The task of spatial-temporal action detection has attracted increasing researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of long-term information and are prone to inefficiency. In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. There are two key characteristics in this framework: (1) Both long-term and short-term sampled information are explicitly utilized in our spatio-temporal network, (2) A new dynamic feature sampling module (DTS) is designed to effectively approximate the tube output while keeping the system tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets, achieving promising results that are competitive to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our framework about 7.6 times more efficient than the nearest competitor.

Spatio-Temporal Action Detector with Self-Attention

2021 ◽  
Author(s):  
Xurui Ma ◽  
Zhigang Luo ◽  
Xiang Zhang ◽  
Qing Liao ◽  
Xingyu Shen ◽  
...  
Keyword(s):  
Spatio Temporal ◽  
Temporal Action

scholarly journals Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1657 ◽  
Author(s):  
Le Wang ◽  
Xuhuan Duan ◽  
Qilin Zhang ◽  
Zhenxing Niu ◽  
Gang Hua ◽  
...  
Keyword(s):  
Action Localization ◽  
Spatio Temporal ◽  
Temporal Action

scholarly journals Action Tubelet Detector for Spatio-Temporal Action Localization

2017 ◽  
Author(s):  
Vicky Kalogeiton ◽  
Philippe Weinzaepfel ◽  
Vittorio Ferrari ◽  
Cordelia Schmid
Keyword(s):  
Action Localization ◽  
Spatio Temporal ◽  
Temporal Action

scholarly journals Standardised Framework for Quantitative Analysis of Fibrillation Dynamics

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xinyang Li ◽  
Caroline H. Roney ◽  
Balvinder S. Handa ◽  
Rasheda A. Chowdhury ◽  
Steven A. Niederer ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Open Source Software ◽  
Spiral Waves ◽  
Data Sets ◽  
Research Groups ◽  
Phase Singularity ◽  
Custom Made ◽  
Spatio Temporal ◽  
Comprehensive Framework ◽  
Temporal Action

Abstract The analysis of complex mechanisms underlying ventricular fibrillation (VF) and atrial fibrillation (AF) requires sophisticated tools for studying spatio-temporal action potential (AP) propagation dynamics. However, fibrillation analysis tools are often custom-made or proprietary, and vary between research groups. With no optimal standardised framework for analysis, results from different studies have led to disparate findings. Given the technical gap, here we present a comprehensive framework and set of principles for quantifying properties of wavefront dynamics in phase-processed data recorded during myocardial fibrillation with potentiometric dyes. Phase transformation of the fibrillatory data is particularly useful for identifying self-perpetuating spiral waves or rotational drivers (RDs) rotating around a phase singularity (PS). RDs have been implicated in sustaining fibrillation, and thus accurate localisation and quantification of RDs is crucial for understanding specific fibrillatory mechanisms. In this work, we assess how variation of analysis parameters and thresholds in the tracking of PSs and quantification of RDs could result in different interpretations of the underlying fibrillation mechanism. These techniques have been described and applied to experimental AF and VF data, and AF simulations, and examples are provided from each of these data sets to demonstrate the range of fibrillatory behaviours and adaptability of these tools. The presented methodologies are available as an open source software and offer an off-the-shelf research toolkit for quantifying and analysing fibrillatory mechanisms.

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