scholarly journals Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Bardia Yousefi ◽  
Chu Kiong Loo
Keyword(s):  
Optical Flow ◽  
Human Action Recognition ◽  
General Characteristic ◽  
Human Action ◽  
Biological Movement ◽  
Movement Recognition ◽  
Proposed Model ◽  
The Brain ◽  
Flow Division ◽  
Form Information

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

scholarly journals Human Action Recognition Based on Fusion Features Extraction of Adaptive Background Subtraction and Optical Flow Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shaoping Zhu ◽  
Limin Xia
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Background Subtraction ◽  
Flow Model ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Human Action ◽  
Multiple Instance Learning ◽  
Data Sets ◽  
Flow Feature

A novel method based on hybrid feature is proposed for human action recognition in video image sequences, which includes two stages of feature extraction and action recognition. Firstly, we use adaptive background subtraction algorithm to extract global silhouette feature and optical flow model to extract local optical flow feature. Then we combine global silhouette feature vector and local optical flow feature vector to form a hybrid feature vector. Secondly, in order to improve the recognition accuracy, we use an optimized Multiple Instance Learning algorithm to recognize human actions, in which an Iterative Querying Heuristic (IQH) optimization algorithm is used to train the Multiple Instance Learning model. We demonstrate that our hybrid feature-based action representation can effectively classify novel actions on two different data sets. Experiments show that our results are comparable to, and significantly better than, the results of two state-of-the-art approaches on these data sets, which meets the requirements of stable, reliable, high precision, and anti-interference ability and so forth.

scholarly journals Motion boundary emphasised optical flow method for human action recognition

2020 ◽  
Vol 14 (6) ◽  
pp. 378-390
Author(s):  
Cheng Peng ◽  
Haozhi Huang ◽  
Ah-Chung Tsoi ◽  
Sio-Long Lo ◽  
Yun Liu ◽  
...  
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Flow Method ◽  
Optical Flow Method ◽  
Motion Boundary

scholarly journals Combining Spatio-Temporal Appearance Descriptors and Optical Flow for Human Action Recognition in Video Data

2014 ◽  
Author(s):  
Karla Brkić ◽  
Srđan Rašić ◽  
Axel Pinz ◽  
Siniša Šegvić ◽  
Zoran Kalafatić
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Spatio Temporal

Human Action Recognition Using Optical Flow Accumulated Local Histograms

2009 ◽  
pp. 32-39 ◽  
Author(s):  
Manuel Lucena ◽  
Nicolás Pérez de la Blanca ◽  
José Manuel Fuertes ◽  
Manuel Jesús Marín-Jiménez
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

An Action Recognition Model Based on the Bayesian Networks

2014 ◽  
Vol 513-517 ◽  
pp. 1886-1889
Author(s):  
Wen Qiang Chen ◽  
Guo Qiang Xiao ◽  
Xiao Qin Tang
Keyword(s):  
Bayesian Networks ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Bayesian Network Model ◽  
Recognition Method ◽  
Proposed Model ◽  
Maximum A Posterior ◽  
Spatio Temporal ◽  
High Level

In this paper, we propose a novel human action recognition method based on Bayesian network model and high-level semantic concept (human action attribute). Firstly, we extract 3D-SIFT descriptors for each spatio-temporal interest point in the videos. Secondly, the bag of words is used as the low-level feature to represent these videos. Finally, Bayesian networks related to action attributes are trained based on MAP (Maximum A Posterior Probability) to recognize human behavior. The experimental results show that the proposed model is effective on action recognition.

scholarly journals An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Li Li
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Feature Fusion ◽  
Human Action Recognition ◽  
Development Trend ◽  
Human Action ◽  
Multimedia Technology ◽  
Score Vector ◽  
Flow Image ◽  
Fully Connected

In accordance with the development trend of competitive aerobics’ arrangement structure, this paper studies the online arrangement method of difficult actions in competitive aerobics based on multimedia technology to improve the arrangement effect. RGB image, optical flow image, and corrected optical flow image are taken as the input modes of difficult action recognition network in competitive aerobics video based on top-down feature fusion. The key frames of input modes in competitive aerobics video are extracted by using the key frame extraction method based on subshot segmentation of a double-threshold sliding window and fully connected graph. Through forward propagation, the score vector of video relative to all categories is obtained, and the probability score of probability distribution is obtained after normalization. The human action recognition in competitive aerobics video is completed, and the online arrangement of difficult action in competitive aerobics is realized based on this. The experimental results show that this method has a high accuracy in identifying difficult actions in competitive aerobics video; the online arrangement of difficult actions in competitive aerobics has obvious advantages, meets the needs of users, and has strong practicability.

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