Real time human action recognition using triggered frame extraction and a typical CNN heuristic

Mishra, Soumya Ranjan; Mishra, Tusar Kanti; Sanyal, Goutam; Sarkar, Anirban; Satapathy, Suresh Chandra

doi:10.1016/j.patrec.2020.04.031

Cited by 34 publications

(11 citation statements)

References 15 publications

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“…In the actual calculation process, the number of image blocks will have different degrees of influence on the recall rate and clustering time of the algorithm, and the experimental test is carried out on it. e human motion recognition method based on spatiotemporal image segmentation and interactive region detection proposed in literature [1] was taken as Method 1. e video human motion recognition method based on the CNN feature of the training graph proposed in literature [3] was taken as Method 2, and the specific experimental comparison results are shown in Tables 1-3.…”

Section: Comparison Of Image Deredundancy Processing Resultsmentioning

confidence: 99%

“…In terms of image redundancy, the wrong matching means that the correct image may be deleted, and at the same time, it will bring a series of economic losses to users [3]. In order to better deredundancy processing of the image, the following research is conducted in combination with data deredundancy technology to effectively overcome the deficiency of the deredundancy method and greatly improve the retrieval accuracy.…”

Section: Image Deredundancy Processing Based On Data Deredundancy Technologymentioning

confidence: 99%

“…Currently, humans can recognize most movements at a glance, but some remain elusive. Human movements can be set as a series of orderly poses, and there are great differences between different people or the same people when they perform the same actions under different circumstances [3]. As the types of human movements continue to increase, the overlap between each class will also increase, which brings a certain difficulty for human movement recognition.…”

Section: Introductionmentioning

confidence: 99%

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Research on Human Motion Recognition Based on Data Redundancy Technology

2021

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Aiming at the problems of low recognition rate and slow recognition speed of traditional body action recognition methods, a human action recognition method based on data deduplication technology is proposed. Firstly, the data redundancy technology and perceptual hashing technology are combined to form an index, and the image is filtered from the structure, color, and texture features of human action image to achieve image redundancy processing. Then, the depth feature of processed image is extracted by depth motion map; finally, feature recognition is carried out by convolution neural network so as to achieve the purpose of human action recognition. The simulation results show that the proposed method can obtain the optimal recognition results and has strong robustness. At the same time, it also fully proves the importance of human motion recognition.

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Section: Comparison Of Image Deredundancy Processing Resultsmentioning

confidence: 99%

Section: Image Deredundancy Processing Based On Data Deredundancy Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Human Motion Recognition Based on Data Redundancy Technology

2021

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“…CNN has been proven to have great advantages in a variety of classification tasks, such as image recognition, natural language processing, and human action recognition (25)(26)(27)(28)(29)(30). In recent years, a number of excellent CNN classification models have been created, such as AlexNet (31) and VGGNet (32).…”

Section: Network Structurementioning

confidence: 99%

Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

Shi

Xu³

et al. 2021

Front. Public Health

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Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.

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“…Camera-based monitoring systems [ 38 , 46 , 47 , 48 , 49 ] solve the problem of having an inconvenient wearable device attached to one’s body or skin. Although various HAR models have been suggested for recognizing ADLs, those based on ML are now playing a major role [ 22 , 48 , 49 , 50 , 51 , 52 ]. An example is HAR based on a dynamic Bayesian network for detecting the abnormal actions of elderly people from camera video [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN

Tan

Badarch

Zeng

et al. 2021

Sensors

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The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident’s house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.

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Real time human action recognition using triggered frame extraction and a typical CNN heuristic

Cited by 34 publications

References 15 publications

Research on Human Motion Recognition Based on Data Redundancy Technology

Research on Human Motion Recognition Based on Data Redundancy Technology

Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN

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