Skip to main content
SpringerLink
Log in

Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance Applications

  • Conference paper
  • First Online:
Innovations in Computer Science and Engineering (ICICSE 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 565))

Abstract

We propose a CNN-based framework for “real-time object detection and tracking using deep learning” in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance benchmarks in terms of accuracy has changed. The data processing is handled by industry buzzwords: deep learning (DL) and computer vision (CV). The CNN-based framework uses the single object tracker value to match arrival models and find targets in the next frame. Simply applying single object tracking to multiple object tracking will encounter problems in computational efficiency and results due to occlusion. In this paper, we introduce a “spatial attention mechanism (STAM)” to manage occlusion bias and target interaction. Object tracking is a sensational technology in image processing with great future implications. Multiple object tracking (MOT) has seen an extensive boom in the last few years due to machine learning, deep learning, computer vision, and more. This paper aims to provide an object tracking software solution. Using YOLO’s “You Only Look Once” technology with the help of Tensor flow, the system is geared toward object detection, tracking, and counting. Proven, effective detection and tracking on various dataset. Algorithms that offer real-time, accurate, and precise identifications appropriate for real-time applications.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nguyen VD et al (2017) Learning framework for robust obstacle detection, recognition, and tracking. IEEE Trans Intell Transport Syst 18(6):1633–1646

    Google Scholar 

  2. Kain Z et all (2018) Detecting abnormal events in university areas. In: 2018 International conference on computer and applications (ICCA), pp 260–264

    Google Scholar 

  3. Wang P et al (2018) Detection of unwanted traffic congestion based on existing surveillance system using in freeway via a CNN-architecture trafficnet. In: IEEE conference on industrial electronics and applications (ICIEA), Wuhan, 2018, pp 1134–1139

    Google Scholar 

  4. Mu Q, Wei Y, Liu Y, Li Z (2018) The research of target tracking algorithm based on an improved PCANet. In: 10th international conference on intelligent human-machine systems and cybernetics (IHMSC), Hangzhou, 2018, pp 195–199

    Google Scholar 

  5. Baykara HC et al (2017) Real-time detection, tracking and classification of multiple moving objects in UAV videos. In: 29th IEEE international conference on tools with artificial intelligence (ICTAI), Boston, MA, 2017, pp 945–950

    Google Scholar 

  6. Wang W, Shi M, Li W (2017) Object tracking with shallow convolution feature. In: 9th international conference on intelligent human-machine systems and cybernetics (IHMSC), Hangzhou, 2017, pp 97–100

    Google Scholar 

  7. Muhammad K et al (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6:18174–18183

    Google Scholar 

  8. Hernandez DE et al (2018) Cell tracking with deep learning and the Viterbi algorithm. In: International conference on manipulation, automation and robotics at small scales (MARSS), Nagoya, 2018, pp 1–6

    Google Scholar 

  9. Qian X et al (2017) An object tracking method using deep learning and adaptive particle filter for night fusion image. In: 2017 International conference on progress in informatics and computing (PIC), Nanjing, 2017, pp 138–142

    Google Scholar 

  10. Yoon Y et al (2018) Online multi-object tracking using selective deep appearance matching. In: IEEE international conference on consumer electronics—Asia (ICCE-Asia), Jeju, pp 206–212

    Google Scholar 

  11. Bharadwaj HS, Biswas S, Ramakrishnan KR (2016) A large scale dataset for classification of vehicles in urban traffic scenes. In: Proceedings of the 10th Indian conference on computer vision, graphics and image processing, ACM

    Google Scholar 

  12. Mohana et al, Performance evaluation of background modeling methods for object detection and tracking. In: International conference on inventive systems and control (ICISC)

    Google Scholar 

  13. Chandan G et al (2018) Real time object detection and tracking using deep learning and OpenCV. In: International conference on inventive research in computing applications (ICIRCA)

    Google Scholar 

  14. Mohana et al, Elegant and efficient algorithms for real time object detection, counting and classification for video surveillance applications from single fixed camera. In: International conference on circuits, controls, communications and computing (I4C)

    Google Scholar 

  15. Mohana et al, Simulation of object detection algorithms for video surveillance applications. In: 2nd international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)

    Google Scholar 

  16. Raghunandan A et al,Object detection algorithms for video surveillance applications. In: International conference on communication and signal processing (ICCSP)

    Google Scholar 

  17. Mangawati A et al, Object tracking algorithms for video surveillance applications. In: 2018 international conference on communication and signal processing (ICCSP)

    Google Scholar 

  18. Mohana et al, Design and implementation of object detection, tracking, counting and classification algorithms using artificial intelligence for automated video surveillance applications. In: Advanced computing and communication society (ACCS)—24th annual international conference on advanced computing and communications (ADCOM-2018), IIITB, Bangalore

    Google Scholar 

  19. Jo KU, Im JH, Kim J, Kim DS (2017) A real-time multi-class multi-object tracker using YOLOv2. In: IEEE ICSIPA, Malaysia, September 12–14

    Google Scholar 

  20. S Sanjana VR Shriya G Vaishnavi K Ashwini 2021 A review on various methodologies used for vehicle classification, helmet detection and number plate recognition Evol Intel 14 2 979 987

    Article  Google Scholar 

  21. Kusuma T, Ashwini K (2021) Modular ST-MRF environment for moving target detection and tracking under adverse local conditions. In: International conference on big data analytics. Springer, Cham, pp 93–105

    Google Scholar 

  22. Kusuma T, Ashwini K (2018) Real time object tracking in H. 264/AVC using polar vector median and block coding modes. Int J Comp Inform Eng 12(11):981–985

    Google Scholar 

  23. Kodipalli A, Devi S (2021) Prediction of PCOS and mental health using fuzzy inference and SVM. Frontiers in Public Health

    Google Scholar 

  24. Kusuma T, Ashwini K (2022) Analysis of deep learning frameworks for object detection in motion. Int J Knowl Based Intell Eng Syst. ISSN:1327-2314. https://doi.org/10.3233/kes-220002

  25. Kusuma T, Ashwini K (2022) Multiple object tracking using STMRF and YOLOv4 deep SORT in surveillance video. Int J Res Trends Innov. ISSN:2456-3315

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Global Academy of Technology, Bangalore, India

    T. Kusuma & K. Ashwini

Authors
  1. T. Kusuma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Ashwini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Kusuma .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions, Ibrahimpatnam, India

    Rishi Sayal

  3. Jawaharlal Nehru Technological University, Hyderabad, Telangana, India

    A. Govardhan

  4. Cloud Computing, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kusuma, T., Ashwini, K. (2023). Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance Applications. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, vol 565. Springer, Singapore. https://doi.org/10.1007/978-981-19-7455-7_56

Download citation

Publish with us

Policies and ethics

Search

Navigation