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A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications

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Abstract

Human Action Recognition (HAR) involves human activity monitoring task in different areas of medical, education, entertainment, visual surveillance, video retrieval, as well as abnormal activity identification, to name a few. Due to an increase in the usage of cameras, automated systems are in demand for the classification of such activities using computationally intelligent techniques such as Machine Learning (ML) and Deep Learning (DL). In this survey, we have discussed various ML and DL techniques for HAR for the years 2011–2019. The paper discusses the characteristics of public datasets used for HAR. It also presents a survey of various action recognition techniques along with the HAR applications namely, content-based video summarization, human–computer interaction, education, healthcare, video surveillance, abnormal activity detection, sports, and entertainment. The advantages and disadvantages of action representation, dimensionality reduction, and action analysis methods are also provided. The paper discusses challenges and future directions for HAR.

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Abbreviations

ABC:

Artificial Bee Colony

ADI:

Average Depth Image

ADL:

Activities of Daily Living

AGC:

Adaptive Graph Convolution

AGCN:

Adaptive Graph Convolutional Network

ANN:

Artificial Neural Network

ARA:

Average Recognition Accuracy

ASAGA:

Adaptive Simulated Annealing Genetic Algorithm

BN:

Batch Normalization

BoVW:

Bag of Visual Words

BPTT:

Back-Propagation-Through-Time

CAE:

Convolution Autoencoder

CHMM:

Coupled Hidden Markove Model

CNN:

Convolution Neural Network

CS:

Cross-Subject

CV:

Cross-View

DBN:

Deep Belief Network

DDI:

Depth Difference Image

DDS:

Depth Differential Silhouettes

DE:

Differential Evolution

DL:

Deep Learning

DMM:

Depth Motion Map

DNN:

Deep Neural Network

DRNN:

Differential Recurrent Neural Network

DT:

Decision Tree

DTW:

Dynamic Time Warping

ELM:

Extreme Learning Machine

FCN:

Fully Convolutional Network

FTP:

Fourier Temporal Pyramid

GA:

Genetic Algorithm

GAN:

Generative Adversarial Network

GDI:

Geodesic Distance Iso

GLCM:

Grey Level Co-occurrence Matrix

GRU:

Gated Recurrent Unit

HAR:

Human Action Recognition

HCI:

Human–Computer Interface

HMM:

Hidden Markov Model

HOF:

Histogram of Optical Flow

HOG:

Histogram of Oriented Gradient

HoMB:

Histogram of Motion Boundary

HoVW:

Histogram of Visual Word

IEF:

Iterative Error Feedback

JDM:

Joint Distance Map

KDA:

Kernel Discriminant Analysis

KELM:

Kernel Extreme Learning Machine

kNN:

k-Nearest Neighbor

KPCA:

Kernel PCA

LBP:

Local Binary Pattern

LBPH:

LBP Histogram

LDA:

Linear Discriminant Analysis

LHMM:

Layered Hidden Markove Model

LOAO:

Leave One Actor Out

LOSO:

Leave One Sequence Out

LSTM:

Long Short-Term Memory

MAP:

Mean Average Precision

MEI:

Motion Energy Image

MHI:

Motion History Image

MiCT:

Mixed Convolution Neural Network

ML:

Machine Learning

MSE:

Mean Squared Error

NBNN:

Naïve Bayes Nearest Neighbor

PCA:

Principal Component Analysis

PCOG:

Pyramid Correlogram of Oriented Gradients

PoF2I:

Pose Feature to Image

PSO:

Particle Swarm Optimization

PSO-WC:

PSO-Weight Class

PSO-WV:

PSO-Weight Views

RBD:

Reduced Basis Decomposition

RBF:

Radial Basis Function

RBM:

Restricted Boltzman Machine

RF:

Random Forest

RNN:

Recurrent Neural Network

ROI:

Region of Interest

RVM:

Relevance Vector Machine

RVM:

Relevance Vector Machine

SDEG:

Spatial Edge Distribution of Gradients

SDK:

Software Development Kit

sDTD:

sequential Deep Trajectory Descriptor

SIFT:

Scale Invariant Feature Transform

SPD:

Symmetric Positive Definite

SSM:

Self-Similarity Matrix

STIP:

Space–Time Interest Point

STM:

Spatio-Temporal Matrix

SVM:

Support Vector Machine

TDD:

Two-stream Deep Convolution Descriptor

TpDD:

Trajectory-pooled Deep-Convolutional Descriptor

TS-GCN:

Two-Stream Graph Convolutional Network

TSN:

Temporal Segment Network

WLNBNN:

Weighted Local NBNN

ZSAR:

Zero-Shot Action Recognition

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    Preksha Pareek & Ankit Thakkar

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Pareek, P., Thakkar, A. A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications. Artif Intell Rev 54, 2259–2322 (2021). https://doi.org/10.1007/s10462-020-09904-8

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