Comparative study of orthogonal moments for human postures recognition
References
[1]
Abobakr A., Nahavandi D., Iskander J., Hossny M., Nahavandi S., Smets M., RGB-D human posture analysis for ergonomie studies using deep convolutional neural network, in: 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC, 2017, pp. 2885–2890.
[2]
Amine Elforaici M.E., Chaaraoui I., Bouachir W., Ouakrim Y., Mezghani N., Posture recognition using an RGB-D camera: Exploring 3D body modeling and deep learning approaches, in: 2018 IEEE Life Sciences Conference, LSC, 2018, pp. 69–72.
[3]
Arun D.K., Sumukh Subramanya H.K., Goel T., Tanush N., Nayak J.S., Video-based elderly fall detection using convolutional neural networks, in: Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems, Springer Nature, Singapore, 2022, pp. 803–814.
[4]
Asli B.H.S., Flusser J., Fast computation of Krawtchouk moments, Inform. Sci. 288 (2014) 73–86.
[5]
Boulay B., Brémond F., Thonnat M., Applying 3D human model in a posture recognition system, Pattern Recognit. Lett. 27 (2006) 1788–1796. Vision for Crime Detection and Prevention.
[6]
Bourennane S., Fossati C., Comparison of shape descriptors for hand posture recognition in video, Signal Image Video Process. 6 (2012) 147–157.
[7]
Brulin D., Benezeth Y., Courtial E., Posture recognition based on fuzzy logic for home monitoring of the elderly, IEEE Trans. Inf. Technol. Biomed. 16 (2012) 974–982.
[8]
Buccolieri F., Distante C., Leone A., Human posture recognition using active contours and radial basis function neural network, in: IEEE Conference on Advanced Video and Signal Based Surveillance, 2005, pp. 213–218.
[9]
Cao B., Bi S., Zheng J., Yang D., Human posture recognition using skeleton and depth information, in: 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA), 2018, pp. 275–280.
[10]
Chandrashekar G., Sahin F., A survey on feature selection methods, Comput. Electr. Eng. 40 (2014) 16–28.
[11]
Chen S., Akselrod P., Zhao B., Carrasco J.A.P., Linares-Barranco B., Culurciello E., Efficient feedforward categorization of objects and human postures with address-event image sensors, IEEE Trans. Pattern Anal. Mach. Intell. 34 (2012) 302–314.
[12]
Chen C., Liang J., Zhao H., Hu H., Tian J., Frame difference energy image for gait recognition with incomplete silhouettes, Pattern Recognit. Lett. 30 (2009) 977–984.
[13]
Chen S., Tang W., Zhang X., Culurciello E., A 64 × 64 pixels UWB wireless temporal-difference digital image sensor, IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 20 (2012) 2232–2240.
[14]
Cucchiara R., Grana C., Prati A., Vezzani R., Probabilistic posture classification for Human-behavior analysis, IEEE Trans. Syst. Man Cybern. A 35 (2005) 42–54.
[15]
De A., Saha A., Kumar P., Pal G., Fall detection method based on Spatio-temporal feature fusion using combined two-channel classification, Multimedia Tools Appl. 81 (2022) 26081–26100.
[16]
Dhiman C., Vishwakarma D.K., A review of state-of-the-art techniques for abnormal human activity recognition, Eng. Appl. Artif. Intell. 77 (2019) 21–45.
[17]
Ding W., Hu B., Liu H., Wang X., Huang X., Human posture recognition based on multiple features and rule learning, Int. J. Mach. Learn. Cybern. 11 (2020) 2529–2540.
[18]
Divya R., Peter J.D., Smart healthcare system-a brain-like computing approach for analyzing the performance of detectron2 and PoseNet models for anomalous action detection in aged people with movement impairments, Complex Intell. Syst. 8 (2022) 3021–3040.
[19]
El Alami A., Berrahou N., Lakhili Z., Mesbah A., Berrahou A., Qjidaa H., Efficient color face recognition based on quaternion discrete orthogonal moments neural networks, Multimedia Tools Appl. 81 (2022) 7685–7710.
[20]
El-Soud M.W.A., Zyout I., Hosny K.M., Eltoukhy M.M., Fusion of orthogonal moment features for mammographic mass detection and diagnosis, IEEE Access 8 (2020) 129911–129923.
[21]
Fan K., Wang P., Zhuang S., Human fall detection using slow feature analysis, Multimedia Tools Appl. 78 (2019) 9101–9128.
[22]
Feng G., Lin Q., Design of elder alarm system based on body posture reorganization, in: International Conference on Anti-Counterfeiting, Security and Identification, 2010, pp. 249–252.
[23]
Goldmann L., Karaman M., Sikora T., Human body posture recognition using MPEG-7 descriptors, in: Visual Communications and Image Processing, International Society for Optics and Photonics, 2004, pp. 177–189.
[24]
Gorji H.T., Haddadnia J., A novel method for early diagnosis of alzheimer’s disease based on pseudo zernike moment from structural MRI, Neuroscience 305 (2015) 361–371.
[25]
Goyal K., Singhai J., Review of background subtraction methods using Gaussian mixture model for video surveillance systems, Artif. Intell. Rev. 50 (2018) 241–259.
[26]
Hachaj T., Ogiela M.R., Rule-based approach to recognizing human body poses and gestures in real time, Multimedia Syst. 20 (2014) 81–99.
[27]
Haritaoglu I., Harwood D., Davis L.S., Ghost: a human body part labeling system using silhouettes, in: 14th International Conference on Pattern Recognition, 1998, pp. 77–82.
[28]
Hasib R., Khan K.N., Yu M., Khan M.S., Vision-based human posture classification and fall detection using convolutional neural network, in: 2021 International Conference on Artificial Intelligence, ICAI, 2021, pp. 74–79.
[29]
Hoa N.T., Bui T.D., Classifying human body postures by a two-neuron fuzzy neural network, in: IEEE International Conference on Computing Communication Technologies, Research, Innovation, and Vision for the Future, RIVF, 2016, pp. 142–146.
[30]
Hosny K.M., Exact Legendre moment computation for gray level images, Pattern Recognit. 40 (2007) 3597–3605.
[31]
Hosny K.M., New set of gegenbauer moment invariants for pattern recognition applications, Arab. J. Sci. Eng. 39 (2014) 7097–7107.
[32]
Hosny K.M., Papakostas G.A., Koulouriotis D.E., Accurate reconstruction of noisy medical images using orthogonal moments, in: 18th International Conference on Digital Signal Processing, 2013, pp. 1–6.
[33]
Hsieh J.-W., Chuang C.-H., Chen S.-Y., Chen C.-C., Fan K.-C., Segmentation of human body parts using deformable triangulation, IEEE Trans. Syst. Man Cybern. A 40 (2010) 596–610.
[34]
Hsieh Y.-H., Hidayati S.C., Cheng W.-H., Hu M.-C., Hua K.-L., Who’s the best charades player? Mining iconic movement of semantic concepts, in: International Conference on Multimedia Modeling, in: Lecture Notes in Computer Science, Springer International Publishing, 2014, pp. 231–241.
[35]
Hu J., Su T., Lin P., 3-D human posture recognition system using 2-D shape features, in: IEEE International Conference on Robotics and Automation, 2007, pp. 3933–3938.
[36]
Huang X., Wang F., Zhang J., Hu Z., Jin J., A posture recognition method based on indoor positioning technology, Sensors 19 (2019) 1464.
[37]
Huu P.N., Thi N.N., Ngoc T.P., Proposing posture recognition system combining MobilenetV2 and LSTM for medical surveillance, IEEE Access 10 (2022) 1839–1849.
[38]
Hwang C.-L., Chen B.-L., Huang H.-H., Syu H.-T., Hybrid learning model and MMSVM classification for on-line visual imitation of a human with 3-D motions, in: Robotics and Autonomous Systems, Emerging Spatial Competences: From Machine Perception to Sensorimotor Intelligence, Vol. 71, 2015, pp. 150–165.
[39]
Iazzi A., Rziza M., Oulad Haj Thami R., Fall detection system-based posture-recognition for indoor environments, J. Imaging 7 (2021) 42.
[40]
Idris M.I., Zabidi A., Yassin I.M., Ali M.S.A.M., Human posture recognition using android smartphone and artificial neural network, in: IEEE 6th Control and System Graduate Research Colloquium, 2015, pp. 120–124.
[41]
Jalal A., Akhtar I., Kim K., Human posture estimation and sustainable events classification via pseudo-2D stick model and K-ary tree hashing, Sustainability 12 (2020) 9814.
[42]
Jazzar M.M., Muhammad G., Feature selection based verification/identification system using fingerprints and palm print, Arab. J. Sci. Eng. 38 (2013) 849–857.
[43]
Jiang H., Li Z., Drew M.S., Recognizing posture in pictures with successive convexification and linear programming, IEEE MultiMedia 14 (2007) 26–37.
[44]
Juang C., Chang C., Human body posture classification by a neural fuzzy network and home care system application, IEEE Trans. Syst. Man Cybern. A 37 (2007) 984–994.
[45]
Juang C., Chang C., Wu J., Lee D., Computer vision-based human body segmentation and posture estimation, IEEE Trans. Syst. Man Cybern. A 39 (2009) 119–133.
[46]
Juang C.-F., Chen T.-C., Du W.-C., Human body 3D posture estimation using significant points and two cameras, Sci. World J. 2014 (2014).
[47]
Juang C.-F., Ni W.-E., Human posture classification using interpretable 3D fuzzy body voxel features and hierarchical fuzzy classifiers, IEEE Trans. Fuzzy Syst. (1) (2022).
[48]
Kamel A., Sheng B., Yang P., Li P., Shen R., Feng D.D., Deep convolutional neural networks for human action recognition using depth maps and postures, IEEE Trans. Syst. Man Cybern.: Syst. 49 (2019) 1806–1819.
[49]
Kang H.-G., Lee S.-H., Human body posture recognition with discrete cosine transform, in: International Conference on Big Data and Smart Computing, IEEE, 2016, pp. 423–426.
[50]
Kaur B., Singh S., Kumar J., Iris recognition using zernike moments and polar harmonic transforms, Arab. J. Sci. Eng. 43 (2018) 7209–7218.
[51]
Kepski M., Kwolek B., Embedded system for fall detection using body-worn accelerometer and depth sensor, in: 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2015, pp. 755–759.
[52]
Le T., Nguyen M., Nguyen T., Human posture recognition using human skeleton provided by kinect, in: International Conference on Computing, Management and Telecommunications, 2013, pp. 340–345.
[53]
Li C., Chen Y., Human posture recognition by simple rules, in: IEEE International Conference on Systems, Man and Cybernetics. Presented at the 2006 IEEE International Conference on Systems, Man and Cybernetics, 2006, pp. 3237–3240.
[54]
Li Q., Stankovic J.A., Hanson M.A., Barth A.T., Lach J., Zhou G., Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information, in: 6th International Workshop on Wearable and Implantable Body Sensor Networks, 2009, pp. 138–143.
[55]
Li H., Sun Q., The recognition of moving human body posture based on combined neural network, in: IEEE Conference Anthology. Presented at the IEEE Conference Anthology, 2013, pp. 1–5.
[56]
Li M., Zhang G., Image multi-human behavior analysis based on low rank texture direction, J. Signal Process. Syst. 90 (2018) 1245–1255.
[57]
Ma X., Wang H., Xue B., Zhou M., Ji B., Li Y., Depth-based human fall detection via shape features and improved extreme learning machine, IEEE J. Biomed. Health Inf. 18 (2014) 1915–1922.
[58]
Ma N., Wu Z., Cheung Y., Guo Y., Gao Y., Li J., Jiang B., A survey of human action recognition and posture prediction, Tsinghua Sci. Technol. 27 (2022) 973–1001.
[59]
Mahmmod B.M., Abdulhussain S.H., Suk T., Hussain A., Fast computation of hahn polynomials for high order moments, IEEE Access 10 (2022) 48719–48732.
[60]
Majumdar I., Chatterji B.N., Kar A., A moment based feature extraction for texture image retrieval, in: Information, Photonics and Communication, Springer, Singapore, 2020, pp. 167–177.
[61]
Manzi A., Cavallo F., Dario P., A neural network approach to human posture classification and fall detection using RGB-D camera, in: Ambient Assisted Living, Lecture Notes in Electrical Engineering, Springer International Publishing, Cham, 2017, pp. 127–139.
[62]
Merrouche F., Baha N., Fall detection based on shape deformation, Multimedia Tools Appl. 79 (2020) 30489–30508.
[63]
Mukherjee D., Jonathan Wu Q.M., Wang G., A comparative experimental study of image feature detectors and descriptors, Mach. Vis. Appl. 26 (2015) 443–466.
[64]
Nadeem A., Jalal A., Kim K., Automatic human posture estimation for sport activity recognition with robust body parts detection and entropy markov model, Multimedia Tools Appl. 80 (2021) 21465–21498.
[65]
Padam Priyal S., Bora P.K., A robust static hand gesture recognition system using geometry based normalizations and krawtchouk moments, Pattern Recognit. 46 (2013) 2202–2219.
[66]
Pandey V.K., Saxena V., Singh J., Robust optical flow estimation using tchebichef moment invariant feature, Arab. J. Sci. Eng. 44 (2019) 6911–6921.
[67]
Pellegrini S., Iocchi L., Human posture tracking and classification through stereo vision and 3D model matching, EURASIP J. Image Video Process. 2008 (2008) 7:1–7:12.
[68]
Qi S., Zhang Y., Wang C., Zhou J., Cao X., A survey of orthogonal moments for image representation: Theory, implementation, and evaluation, ACM Comput. Surv. 55 (2021) 1:1–1:35.
[69]
Salih Abedi W.M., Nadher I., Sadiq A.T., Modified deep learning method for body postures recognition, Int. J. Adv. Sci. Technol. 29 (2020) 3830–3841.
[70]
Sharma S., Aggarwal A., Content-based retrieval of biomedical images using orthogonal Fourier-mellin moments, Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 7 (2019) 286–296.
[71]
Shivashankara S., Srinath S., Signer independent real-time hand gestures recognition using multi-features extraction and various classifiers, Int. J. Inf. Technol. 14 (2022) 1229–1240.
[72]
Singh C., Aggarwal A., An effective approach for noise robust and rotation invariant handwritten character recognition using zernike moments features and optimal similarity measure, Appl. Artif. Intell. 34 (2020) 1011–1037.
[73]
Wang W.-J., Chang J.-W., Haung S.-F., Wang R.-J., Human posture recognition based on images captured by the kinect sensor, Int. J. Adv. Robot. Syst. 13 (2016) 54.
[74]
Wang J., Cheng H., Human posture recognition based on convolutional neural network, in: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2020, Association for Computing Machinery, New York, NY, USA, 2020, pp. 475–480.
[75]
Wang C., Hao Q., Ma B., Wu X., Li J., Xia Z., Gao H., Octonion continuous orthogonal moments and their applications in color stereoscopic image reconstruction and zero-watermarking, Eng. Appl. Artif. Intell. 106 (2021).
[76]
Wang J., Huang Z., Zhang W., Patil A., Patil K., Zhu T., Shiroma E.J., Schepps M.A., Harris T.B., Wearable sensor based human posture recognition, in: IEEE International Conference on Big Data, 2016, pp. 3432–3438.
[77]
Wang X., Liang J., Guo F., Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition, Digit. Signal Process. 34 (2014) 101–107.
[78]
Wee C.-Y., Paramesran R., Mukundan R., Jiang X., Image quality assessment by discrete orthogonal moments, Pattern Recognit. 43 (2010) 4055–4068.
[79]
Wientapper F., Ahrens K., Wuest H., Bockholt U., Linear-projection-based classification of human postures in time-of-flight data, in: IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 559–564.
[80]
Xie F., Xu G., Cheng Y., Tian Y., Human body and posture recognition system based on an improved thinning algorithm, IET Image Process. 5 (2011) 420–428.
[81]
Xing Y., Lv C., Zhang Z., Wang H., Na X., Cao D., Velenis E., Wang F., Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition, IEEE Trans. Comput. Soc. Syst. 5 (2018) 95–108.
[82]
Yang X., Jiang X., A hybrid active contour model based on new edge-stop functions for image segmentation, Int. J. Ambient Comput. Intell. (IJACI) 11 (2020) 87–98.
[83]
Yang J.J., Marler T., Rahmatalla S., Multi-objective optimization-based method for kinematic posture prediction: development and validation, Robotica 29 (2011) 245–253.
[84]
Yap P., Paramesran R., Ong S., Image analysis using hahn moments, IEEE Trans. Pattern Anal. Mach. Intell. 29 (2007) 2057–2062.
[85]
Ye H., Wu P., Zhu T., Xiao Z., Zhang X., Zheng L., Zheng R., Sun Y., Zhou W., Fu Q., Ye X., Chen A., Zheng S., Heidari A.A., Wang M., Zhu J., Chen H., Li J., Diagnosing coronavirus disease 2019 (COVID-19): Efficient Harris Hawks-inspired fuzzy K-nearest neighbor prediction methods, IEEE Access 9 (2021) 17787–17802.
[86]
Yu M., Rhuma A., Naqvi S.M., Wang L., Chambers J., A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment, IEEE Trans. Inf. Technol. Biomed. 16 (2012) 1274–1286.
[87]
Zerrouki N., Harrou F., Sun Y., Houacine A., Vision-based human action classification using adaptive boosting algorithm, IEEE Sens. J. 18 (2018) 5115–5121.
[88]
Zerrouki N., Houacine A., Combined curvelets and hidden Markov models for human fall detection, Multimedia Tools Appl. 77 (2018) 6405–6424.
[89]
Zhang Z., Wang X., Anwar W., Jiang Z.L., A comparison of moments-based logo recognition methods, Abstr. Appl. Anal. 2014 (2014).
[90]
Zhao B., Chen S., Realtime feature extraction using MAX-like convolutional network for human posture recognition, in: IEEE International Symposium of Circuits and Systems. Presented at the 2011 IEEE International Symposium of Circuits and Systems, vol. 267, ISCAS, 2011, pp. 3–2676.
[91]
Zhu H., Liu M., Shu H., Zhang H., Luo L., General form for obtaining discrete orthogonal moments, IET Image Process. 4 (2010) 335–352.
Recommendations
Image analysis by circularly semi-orthogonal moments
Various types of circularly orthogonal moments have been widely used for image reconstruction and rotation invariant classification. However, they suffer from two errors namely numerical integration error and geometric error, which affect their ...
Rotation, scaling and translation invariant texture recognition by Bessel-Fourier moments
The ideal of Bessel-Fourier moments (BFMs) for image analysis and only rotation invariant image cognition has been proposed recently. In this paper, we extend the previous work and propose a new method for rotation, scaling and translation (RST) ...
Radial shifted Legendre moments for image analysis and invariant image recognition
The rotation, scaling and translation invariant property of image moments has a high significance in image recognition. Legendre moments as a classical orthogonal moment have been widely used in image analysis and recognition. Since Legendre moments are ...
Comments
Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 01 April 2023
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Oct 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in