A Comprehensive Survey of Vision-Based Human Action Recognition Methods

Zhang, Hong Bo; Zhang, Yi Xiang; Zhong, Bineng; Lei, Qing; Yang, Lijie; Du, Ji Xiang; Chen, Duan Sheng

doi:10.3390/s19051005

Cited by 384 publications

(213 citation statements)

References 115 publications

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“…Nevertheless, VM signals in our study provided sufficient information for identifying children's activity intensities while indoors with good accuracy. Future studies using computer vision to classify activity intensities in children may consider the use of human activity recognition algorithms [29][30][31] to specifically target sedentary behaviors, such as quiet sitting, from motionless non-sedentary activities, such as quiet standing or performing an isometric bodyweight resistance exercise (e.g., a sustained squat). Furthermore, the estimation of object mass from images is a nontrivial problem [32], which requires additional attention in the computer vision physical activity literature with respect to differential activity intensity classification during human-object interactions.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera

McCullough

Rodríguez

Garber

2020

Sensors

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The purpose of this study was to determine the feasibility and validity of using three-dimensional (3D) video data and computer vision to estimate physical activity intensities in young children. Families with children (2–5-years-old) were invited to participate in semi-structured 20-minute play sessions that included a range of indoor play activities. During the play session, children’s physical activity (PA) was recorded using a 3D camera. PA video data were analyzed via direct observation, and 3D PA video data were processed and converted into triaxial PA accelerations using computer vision. PA video data from children (n = 10) were analyzed using direct observation as the ground truth, and the Receiver Operating Characteristic Area Under the Curve (AUC) was calculated in order to determine the classification accuracy of a Classification and Regression Tree (CART) algorithm for estimating PA intensity from video data. A CART algorithm accurately estimated the proportion of time that children spent sedentary (AUC = 0.89) in light PA (AUC = 0.87) and moderate-vigorous PA (AUC = 0.92) during the play session, and there were no significant differences (p > 0.05) between the directly observed and CART-determined proportions of time spent in each activity intensity. A computer vision algorithm and 3D camera can be used to estimate the proportion of time that children spend in all activity intensities indoors.

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Section: Discussionmentioning

confidence: 99%

Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera

McCullough

Rodríguez

Garber

2020

Sensors

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“…[8] includes an extensive review of wearable camera-based activity recognition works. Other than wearable cameras, visual HAR based on stationary cameras has been extensively studied by the computer vision community and we refer readers to [39] for a survey in this area.…”

Section: Are Imagers the Answer?mentioning

confidence: 99%

Are Accelerometers for Activity Recognition a Dead-end?

Tong

Tailor

Lane

2020

Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications

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Accelerometer-based (and by extension other inertial sensors) research for Human Activity Recognition (HAR) is a dead-end. This sensor does not offer enough information for us to progress in the core domain of HAR-to recognize everyday activities from sensor data. Despite continued and prolonged efforts in improving feature engineering and machine learning models, the activities that we can recognize reliably have only expanded slightly and many of the same flaws of early models are still present today. Instead of relying on acceleration data, we should instead consider modalities with much richer information-a logical choice are images. With the rapid advance in image sensing hardware and modelling techniques, we believe that a widespread adoption of image sensors will open many opportunities for accurate and robust inference across a wide spectrum of human activities.In this paper, we make the case for imagers in place of accelerometers as the default sensor for human activity recognition. Our review of past works has led to the observation that progress in HAR had stalled, caused by our reliance on accelerometers. We further argue for the suitability of images for activity recognition by illustrating their richness of information and the marked progress in computer vision. Through a feasibility analysis, we find that deploying imagers and CNNs on device poses no substantial burden on modern mobile hardware. Overall, our work highlights the need to move away from accelerometers and calls for further exploration of using imagers for activity recognition. 1

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“…In a more recent development, deep-learning based method such as CNN method has received considerable attention in computer vision [56]. The CNN method has been implemented in many fields of image recognition [10,17,[57][58][59].…”

Section: Related Workmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.

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A Comprehensive Survey of Vision-Based Human Action Recognition Methods

Cited by 384 publications

References 115 publications

Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera

Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera

Are Accelerometers for Activity Recognition a Dead-end?

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

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