Skip to main content
Springer Nature Link
Log in

Aka-Net: anchor free-based object detection network for surveillance video transmission in the IOT edge computing environment

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

With the growing use of wireless surveillance cameras in (Internet of things) IoT applications the need to address storage capacity and transmission bandwidth challenges becomes crucial. The majority of successive frames from surveillance cameras contain redundant and irrelevant information, leading to increased transmission burden. Existing video pre-processing techniques often focus on reducing the number of frames without considering accuracy and fail to effectively handle both spatial and temporal redundancies simultaneously. To address these issues, an anchor-free key action point network (AKA-Net) is proposed for video pre-processing in the IoT-edge computing environment. The oriented Features from Accelerated Segment Test (FAST) and rotated Binary Robust Independent Elementary Features (BRIEF) (ORB) feature descriptor is employed to remove duplicate frames, leading to more compact and efficient video representation. The AKA-Net's major contributions include its powerful representation capabilities achieved through the bottleneck module in the information-transferring backbone network, which effectively captures multi-scale features. The information-transferring module helps to improve the performance of the object detection algorithm for video pre-processing by fusing the complementary information from different scales. This allows the algorithm to detect objects of different sizes more accurately, making it highly effective for real-time video pre-processing tasks. Then, the key action point selection module that utilizes the self-attention mechanism is introduced to accurately select informative key action points. This enables efficient network transmission with lower bandwidth requirements, while maintaining high accuracy and low latency. It treats every pixel within the feature map as a temporal-spatial point and leverages self-attention to identify and select the most relevant keypoints. Experiments show that the proposed AKA-Net outperforms existing methods in terms of compression ratio of 54.2% and accuracy with a rate of 96.7%. By addressing spatial and temporal redundancies and optimizing key action point selection, AKA-Net offers a significant advancement in video pre-processing for smart surveillance systems, benefiting various IoT applications.

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

Similar content being viewed by others

References

  1. Sharma R, Sangeetha A (2021) An efficient dimension reduction based fusion of CNN and SVM model for detection of abnormal incident in video surveillance. J Soft Comput Paradigm 3(02):55–69

    Article  Google Scholar 

  2. Qian P, Yuan K, Yao J, Fan C, Zhang H, Liu Y, Lu X (2020) Residual-network-leveraged vehicle-thrown-waste identification in real-time traffic surveillance videos. IEEE Trans Intell Transp Syst 22(3):1817–1826

    Article  Google Scholar 

  3. Mahum R, Irtaza A, Nawaz M, Nazir T, Masood M, Shaikh S, Nasr EA (2022) A robust framework to generate surveillance video summaries using combination of zernike moments and r-transform and deep neural network. Multimed Tools Appl, 1–25.

  4. Muchtar K, Bahri A, Fitria M, Cenggoro TW, Pardamean B, Mahendra A, Munggaran MR, Lin CY (2022) Moving pedestrian localization and detection with guided filtering. IEEE Access 10:89181–89196

    Article  Google Scholar 

  5. Zhou X, Xu X, Liang W, Zeng Z, Yan Z (2021) Deep-learning-enhanced multitarget detection for end–edge–cloud surveillance in smart IoT. IEEE Internet Things J 8(16):12588–12596

    Article  Google Scholar 

  6. Liu W, Hasan I, Liao S (2022) Centre and scale prediction: Anchor-free approach for pedestrian and face detection. Pattern Recogn 135:109071

    Article  Google Scholar 

  7. Cheng G, Wang J, Li K, Xie X, Lang C, Yao Y, Han J (2022) Anchor-free oriented proposal generator for object detection. IEEE Trans Geosci Remote Sens 60:1–11

    Google Scholar 

  8. Dai Z, Yi J, Jiang L, Yang S, Huang X (2021) Cascade centernet: robust object detection for power line surveillance. IEEE Access 9:60244–60257

    Article  Google Scholar 

  9. Zheng Z, Liu W, Wang H, Fan G, Dai Y (2021) Real-time enumeration of metro passenger volume using anchor-free object detection network on edge devices. IEEE Access 9:21593–21603

    Article  Google Scholar 

  10. Ma T, Tian W, Kuang P, Xie Y (2021) An anchor-free object detector with novel corner matching method. Knowl-Based Syst 224:107083

    Article  Google Scholar 

  11. Su H, He Y, Jiang R, Zhang J, Zou W, Fan B (2022) DSLA: dynamic smooth label assignment for efficient anchor-free object detection. Pattern Recogn 131:108868

    Article  Google Scholar 

  12. Zhou Q, Wang J, Liu J, Li S, Ou W, Jin X (2021) RSANet: towards real-time object detection with residual semantic-guided attention feature pyramid network. Mobile Netw Appl 26(1):77–87

    Article  Google Scholar 

  13. Hänel ML, Schönlieb CB (2021) Efficient global optimization of non-differentiable symmetric objectives for multi camera placement. IEEE Sens J 22(6):5278–5287

    Article  Google Scholar 

  14. Yang K, He Z, Pei W, Zhou Z, Li X, Yuan D, Zhang H (2021) Siam corners: Siamese corner networks for visual tracking. IEEE Trans Multimedia 24:1956–1967

    Article  Google Scholar 

  15. Duan K, Xie L, Qi H, Bai S, Huang Q, ian Q (2020) Corner proposal network for anchor-free,two-stage object detection. In: European conference on computer vision, pp 399–416

  16. Zhang Q, Chan AB (2022) Single-frame-based deep view synchronization for unsynchronized multicamera surveillance. IEEE Trans Neural Netw Learn Syst 34(12):10653–10667

    Article  Google Scholar 

  17. Kong T, Sun F, Liu H, Jiang Y, Li L, Shi J (2020) Fovea box: beyond anchor-based object detection. IEEE Trans Image Process 29:7389–7398

    Article  Google Scholar 

  18. Huang Z, Li W, Xia XG, Tao R (2022) A general Gaussian heat map label assignment for arbitrary-oriented object detection. IEEE Trans Image Process 31:1895–1910

    Article  Google Scholar 

  19. Wang G, Wu J, Tian B, Teng S, Chen L, Cao D (2021) CenterNet3D: an anchor free object detector for point cloud. IEEE Trans Intell Transp Syst 23(8):12953–12965

    Article  Google Scholar 

  20. Fan S, Zhu F, Chen S, Zhang H, Tian B, Lv Y, Wang FY (2021) FII-CenterNet: an anchor-free detector with foreground attention for traffic object detection. IEEE Trans Veh Technol 70(1):121–132

    Article  Google Scholar 

  21. Zhou L, Wei H, Li H, Zhao W, Zhang Y, Zhang Y (2020) Arbitrary-oriented object detection in remote sensing images based on polar coordinates. IEEE Access 8:223373–223384

    Article  Google Scholar 

  22. Lin C, Tian D, Duan X, Zhou J, Zhao D, Cao D (2022) 3D-DFM: anchor-free multimodal 3-D object detection with dynamic fusion module for autonomous driving. IEEE Trans Neural Netw Learn Syst 34(12):10812–10822

    Article  Google Scholar 

  23. Chakraborty C, Banerjee A, Garg L, Rodrigues JJ (2020) Internet of medical things for smart healthcare. In: Studies in big data, 80. Cham, Switzerland: Springer.

  24. Sambandam Raju P, Mahalingam M, Arumugam Rajendran R (2019) Design, implementation and power analysis of pervasive adaptive resourceful smart lighting and alerting devices in developing countries supporting incandescent and led light bulbs. Sensors 19(9):2032

    Article  Google Scholar 

  25. Liu Y, Kong L, Chen G, Xu F, Wang Z (2021) Light-weight AI and IoT collaboration for surveillance video pre-processing. J Syst Architect 114:101934

    Article  Google Scholar 

  26. Ke R, Zhuang Y, Pu Z, Wang Y (2020) A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. IEEE Trans Intell Transp Syst 22(8):4962–4974

    Article  Google Scholar 

  27. Chen YY, Lin YH, Hu YC, Hsia CH, Lian YA, Jhong SY (2022) Distributed real-time object detection based on edge-cloud collaboration for smart video surveillance applications. IEEE Access 10:93745–93759

    Article  Google Scholar 

  28. Wisultschew C, Mujica G, Lanza-Gutierrez JM, Portilla J (2021) 3D-LIDAR based object detection and tracking on the edge of IoT for railway level crossing. IEEE Access 9:35718–35729

    Article  Google Scholar 

  29. Xu W, Wu M, Zhu J, Zhao M (2021) Multi-scale skeleton adaptive weighted GCN for skeleton-based human action recognition in IoT. Appl Soft Comput 104:107236

    Article  Google Scholar 

  30. Ullah FU, Muhammad M, Haq K, Khan N, Heidari AA, Baik SW, de Albuquerque VHC (2021) AI-assisted edge vision for violence detection in iot-based industrial surveillance networks. IEEE Trans Industr Inf 18(8):5359–5370

    Article  Google Scholar 

  31. Bouaafia S, Khemiri R, Messaoud S, Ben Ahmed O, Sayadi FE (2022) Deep learning-based video quality enhancement for the new versatile video coding. Neural Comput Appl 34(17):14135–14149

    Article  Google Scholar 

  32. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: 2011 International conference on computer vision, 6, pp 2564–2571. IEEE.

  33. Gao SH, Cheng MM, Zhao K, Zhang XY, Yang MH, Torr P (2019) Res2net: a new multi-scale backbone architecture. IEEE Trans Pattern Anal Mach Intell 43(2):652–662

    Article  Google Scholar 

  34. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, the Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21–37). Springer International Publishing.

  35. Wan S, Ding S, Chen C (2022) Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles. Pattern Recogn 121:108146

    Article  Google Scholar 

  36. Yan G, Woźniak M (2022) Accurate key frame extraction algorithm of video action for aerobics online teaching. Mobile Netw Appl 27(3):1252–1261

    Article  Google Scholar 

  37. Law H, Deng J (2018) Cornernet: Detecting objects as paired keypoints. InProceedings of the European conference on computer vision (ECCV) (pp. 734–750).

  38. Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: Keypoint triplets for object detection. InProceedings of the IEEE/CVF international conference on computer vision (pp. 6569–6578).

Download references

Funding

There is no funding for this study.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Chennai, Tamilnadu, India

    Preethi Sambandam Raju & Murugan Mahalingam

  2. School of Computer Science and Engineering, VIT University, Chennai Campus, Tamilnadu, India

    Revathi Arumugam Rajendran

Authors
  1. Preethi Sambandam Raju
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Revathi Arumugam Rajendran
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Murugan Mahalingam
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Preethi Sambandam Raju.

Ethics declarations

Conflict of interest

Authors declares that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants and/or animals performed by any of the authors.

Informed consent

There is no informed consent for this study.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sambandam Raju, P., Arumugam Rajendran, R. & Mahalingam, M. Aka-Net: anchor free-based object detection network for surveillance video transmission in the IOT edge computing environment. Pattern Anal Applic 27, 56 (2024). https://doi.org/10.1007/s10044-024-01272-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10044-024-01272-1

Keywords