A sparse coded composite descriptor for human activity recognition

Singh, Kulbir; Dhiman, Chhavi; Vishwakarma, Dinesh Kumar; Makhija, Himanshu; Walia, Gurjit Singh

doi:10.1111/exsy.12805

Cited by 11 publications

(3 citation statements)

References 70 publications

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“…Table 5 summarizes the correct recognition rate for Set1-Set4 and the average correct recognition rate which followed by the standard deviation. As Table 5 shows, WSRC-PGM has superior performance compared with TCCA ( Kim & Cipolla, 2008 ), PM ( Lui, 2012 ), gSC and kgSC ( Harandi et al, 2015 ), DMD+SC(SCCD2) ( Singh et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition

Wang

Zhang

2022

PeerJ Computer Science

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It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multi-dimensional data. The recently proposed sparse coding on Grassmann manifold shows high discriminative power in many visual classification tasks. It represents videos on Grassmann manifold using Singular Value Decomposition (SVD) of the data matrix by vectorizing each image in videos, while vectorization destroys the spatial structure of videos. To keep the spatial structure of videos, they can be represented as the form of data tensor. In this paper, we firstly represent human gesture videos on product Grassmann manifold (PGM) by Higher Order Singular Value Decomposition (HOSVD) of data tensor. Each factor manifold characterizes features of human gesture video from different perspectives and can be understood as appearance, horizontal motion and vertical motion of human gesture video respectively. We then propose a weighted sparse coding model on PGM, where weights can be understood as modeling the importance of factor manifolds. Furthermore, we propose an optimization algorithm for learning coding coefficients by embedding each factor Grassmann manifold into symmetric matrices space. Finally, we give a classification algorithm, and experimental results on three public datasets show that our method is competitive to some relevant excellent methods.

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Section: Methodsmentioning

confidence: 99%

A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition

Wang

Zhang

2022

PeerJ Computer Science

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“…The image frame difference was used to detect motion and remove static frames. Singh et al [43] used optical flow and HOG features to specify motion regions. Finally, the extracted features were used to create a sparse-coded composite descriptor to model the action.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

Information

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In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

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“…Human activity recognition is a broad field of study that identifies a person's specific movement or action based on sensor data. It has received significant attention recently because of its wide‐ranging applications in rehabilitation, robotics, surveillance systems, gaming, and many more (Hernández et al, 2014; Piyathilaka & Kodagoda, 2015; Schrader et al, 2020; Singh et al, 2022). HAR is a challenging task due to the complex posture made by humans and the interactions of multiple people.…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning model for human activity recognition using inertial measurement units

Challa,

Kumar,

Semwal

et al. 2023

Expert Systems

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Human activity recognition (HAR) has recently gained popularity due to its applications in healthcare, surveillance, human‐robot interaction, and various other fields. Deep learning (DL)‐based models have been successfully applied to the raw data captured through inertial measurement unit (IMU) sensors to recognize multiple human activities. Despite the success of DL‐based models in human activity recognition, feature extraction remains challenging due to class imbalance and noisy data. Additionally, selecting optimal hyperparameter values for DL models is essential since they affect model performance. The hyperparameter values of some of the existing DL‐based HAR models are chosen randomly or through the trial‐and‐error method. The random selection of these significant hyperparameters may be suitable for some applications, but sometimes it may worsen the model's performance in others. Hence, to address the above‐mentioned issues, this research aims to develop an optimized DL model capable of recognizing various human activities captured through IMU sensors. The proposed DL‐based HAR model combines convolutional neural network (CNN) layers and bidirectional long short‐term memory (Bi‐LSTM) units to simultaneously extract spatial and temporal sequence features from raw sensor data. The Rao‐3 metaheuristic optimization algorithm has been adopted to identify the ideal hyperparameter values for the proposed DL model in order to enhance its recognition performance. The proposed DL model's performance is validated on PAMAP2, UCI‐HAR, and MHEALTH datasets and achieved 94.91%, 97.16%, and 99.25% accuracies, respectively. The results reveal that the proposed DL model performs better than the existing state‐of‐the‐art (SoTA) models.

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A sparse coded composite descriptor for human activity recognition

Cited by 11 publications

References 70 publications

A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition

A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

An optimized deep learning model for human activity recognition using inertial measurement units

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