A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications
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A survey on video‑based Human Action Recognition: recent updates, datasets, challenges, and applications Preksha Pareek1 · Ankit Thakkar1
© Springer Nature B.V. 2020
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. Keywords Human Action Recognition (HAR) · Machine Learning (ML) · Deep Learning (DL) · Challenges in HAR · Public Datasets for HAR · Future directions 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 * Preksha Pareek [email protected] Ankit Thakkar [email protected] 1
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat 382 481, India
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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
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