Skip to main content

Advertisement

Springer Nature Link
Log in

Human and object detection using Hybrid Deep Convolutional Neural Network

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In recent years, human and object detection has increased research in different real-time applications. Due to improvement in the field of deep learning, various methods have been designed for human, object detection and recognition. Hence, Hybrid Deep Convolutional Neural Network (HDCNN) is developed for human and object detection from the video frames. The HDCNN is a combination of Convolutional Neural Network (CNN) and Emperor Penguin Optimization (EPO). Here, EPO is utilized to increase the system parameters of the CNN structure. Initially, pre-processing is applied to eliminate the noise presented in the image and image quality is enhanced. Here, the Gaussian filter is used for the background subtraction in the images. The three different types of databases are considered to validate the proposed methodology. The proposed HDCNN method is tested in MATLAB and compared with existing methods like Deep Neural Network (DNN), CNN and CNN-Firefly Algorithm (FA), respectively. The proposed method is justified with the statistical measurements like accuracy, precision, recall and F-Measure, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ALO:

Ant-Lion Optimization

ANN:

Artificial Neural Networks

CNN:

Convolutional Neural Network

DNN:

Deep Neural Network

DL:

Deep Learning

FA:

Firefly-Algorithm

IoT:

Internet of Things

GA:

Genetic Algorithms

HDCNN:

Hybrid Deep Convolutional Neural Network

IMFF:

Improved Multi-scale Feature Fusion

MODT:

Multi-Object Detection and Tracking

PSO:

Particle Swarm Optimization

RNN:

Recurrent Neural Network

SSD:

Single Shot Multi-box Detector

SVM:

Support Vector Machine

UAV:

Unmanned Aerial Vehicles

VSP-GMN:

Visual Semantic Pose Graph Matrix Network

References

  1. Dey, L., Chakraborty, S., Mukhopadhyay, A.: Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins. Biomed. J. 43(5), 438–450 (2020)

    Article  Google Scholar 

  2. Randhawa, P., Shanthagiri, V., Kumar, A., Yadav, V.: Human activity detection using machine learning methods from wearable sensors. Sensor Rev. (2020)

  3. Minor, E.N., Howard, S.D., Green, A.A.S., Glaser, M.A., Park, C.S., Clark, N.A.: End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy. Soft Matter 16(7), 1751–1759 (2020)

    Article  Google Scholar 

  4. Kousik, N.V., Natarajan, Y., Raja, R.A., Kallam, S., Patan, R., Gandomi, A.H.: Improved salient object detection using hybrid Convolution Recurrent Neural Network. Expert Syst. Appl. 166, 114064 (2020)

    Article  Google Scholar 

  5. Jin, L., Yang, J., Kuang, K., Ni, B., Gao, Y., Sun, Y., Gao, P., et al.: Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet. Biomedicine 62, 103106 (2020)

    Google Scholar 

  6. Jung, M., Chi, S.: Human activity classification based on sound recognition and residual convolutional neural network. Autom. Constr. 114, 103177 (2020)

    Article  Google Scholar 

  7. Fu, K., Zhang, T., Zhang, Y., Sun, X.: OSCD: a one-shot conditional object detection framework. Neurocomputing (2020)

  8. Freire-Obregón, D., Castrillón-Santana, M., Barra, P., Bisogni, C., Nappi, M.: An attention recurrent model for human cooperation detection. Comput. Vis. Image Understand. 102991 (2020)

  9. Xiong, Q., Zhang, J., Wang, P., Liu, D., Gao, R.X.: Transferable two-stream convolutional neural network for human action recognition. J. Manuf. Syst. 56, 605–614 (2020)

    Article  Google Scholar 

  10. Deguchi, M.: Simple and low-cost object detection method based on observation of effective permittivity change. Microelectron. J. 95, 104678 (2020)

    Article  Google Scholar 

  11. Gong, M., Shu, Y.: Real-time detection and motion recognition of human moving objects based on deep learning and multi-scale feature fusion in video. IEEE Access 8, 25811–25822 (2020)

    Article  Google Scholar 

  12. Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., Herrera, Fo.: Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: application in video surveillance. Knowl-Based Syst. 194, 105590 (2020)

    Article  Google Scholar 

  13. Liang, Z., Rojas2†∗, J., Liu, J., Guan, Y.: Visual-semantic-pose graph mixture networks for human-object interaction detection, (2020)

  14. Elhoseny, M.: Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems. Circuits Syst. Signal Process. 39(2), 611–630 (2020)

    Article  Google Scholar 

  15. Matveev, I., Karpov, K., Chmielewski, I., Siemens, E., Yurchenko, A.: Fast object detection using dimensional based features for public street environments. Smart Cities 3(1), 93–111 (2020)

    Article  Google Scholar 

  16. O’Shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. IEEE Trans. Cognit. Commun. Netw. 3(4), 563–575 (2017)

    Article  Google Scholar 

  17. Aceto, G., Ciuonzo, D., Montieri, A., Pescapé, A.: mobile encrypted traffic classification using deep learning: experimental evaluation, lessons learned, and challenges. IEEE Trans. Netw. Serv. Manage. 16(2), 445–458 (2019)

    Article  Google Scholar 

  18. Aceto, G., Ciuonzo, D., Montieri, A., Pescapé, A.: Toward effective mobile encrypted traffic classification through deep learning. Neurocomputing 409, 306–315 (2020)

    Article  Google Scholar 

  19. Mukilan, P., Semunigus, W.: Human object detection: An enhanced black widow optimization algorithm with deep convolution neural network. Neural Comput. Appl. (2021)

  20. Chaudhary, V., Kumar, V.: Fusion of multi-exposure images using recursive and Gaussian filter. Multidimension. Syst. Signal Process. 31(1), 157–172 (2020)

    Article  Google Scholar 

  21. Kun, Fu., Chang, Z., Zhang, Y., Guangluan, Xu., Zhang, K., Sun, X.: Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images. ISPRS J. Photogramm. Remote. Sens. 161, 294–308 (2020)

    Article  Google Scholar 

  22. Harifi, S., Khalilian, M., Mohammadzadeh, J., Ebrahimnejad, S.: Optimizing a neuro-fuzzy system based on nature inspired emperor penguins colony optimization algorithm. IEEE Trans. Fuzzy Syst. (2020)

  23. Harifi, S., Khalilian, M., Mohammadzadeh, J., Ebrahimnejad, S.: Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm. J. Intell. Manuf. 1–15 (2020)

  24. https://motchallenge.net/vis/PETS09-S2L1

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, College of Engineering and Technology, Bule Hora University, Bule Hora, Ethiopia

    P. Mukilan & Wogderess Semunigus

Authors
  1. P. Mukilan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wogderess Semunigus
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to P. Mukilan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mukilan, P., Semunigus, W. Human and object detection using Hybrid Deep Convolutional Neural Network. SIViP 16, 1913–1923 (2022). https://doi.org/10.1007/s11760-022-02151-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02151-0

Keywords