Abstract
The engenderment of thermal imaging techniques intended to provide military surveillance but, the then-nascent stage of this technology witnessed manual human detection. Significant research has been conducted in deep learning algorithms for accurate human detection, yet, so far it is only possible in captured images. In this research, we have explored YOLO, a state of the art algorithm for real-time object detection, in the context of Long Wave Infrared imaging. Exclusive methods for each - human detection and real-time object detection, hold the key to a more sophisticated approach. In pursuit of a unified system, this paper discusses complex localization algorithms for real-time human detection in a thermal feed. The efficacy of the proposed idea has been recorded and reported.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. Computer vision and pattern recognition, arXiv:1804.02767
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, San Diego, CA, USA, 20–25 June 2005, pp 1–8
Chen Y, Han C (2008) Night-time pedestrian detection by visual-infrared video fusion. In: Proceedings of the 7th world congress on intelligent control and automation, Chongqing, China, 25–27 June 2008, pp 5079–5084
Yuan Y, Lu X, Chen X (2015) Multispectral pedestrian detection. Sig Process 110:94–100
Komagal E, Seenivasan V, Anand K, Anand raj CP (2014) Human detection in hours of darkness using Gaussian mixture model algorithm. Int J Inform Sci Tech 4:83–89
Smet T, Nikulin A, Baur J, Frazer, W (2018) Detection and Identification of Remnant PFM-1 ‘Butterfly Mines’ with a UAV-based thermal-imaging protocol. Remote Sens 10. https://doi.org/10.3390/rs10111672
Kim J et al (2017) Convolutional neural network-based human detection in nighttime images using visible light camera sensors. Sensors (Basel, Switzerland) 17(5):1065. https://doi.org/10.3390/s17051065
Scherer D, Muller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: 20th international conference on artificial neural networks (ICANN), Thessaloniki, Greece
Zhang S et al (2018) Single-shot refinement neural network for object detection, computer vision and pattern recognition, arXiv:1711.06897
Budzan S (2015) Human detection in thermal images using low-level features. In: Measurement automation monitoring, June 2015, vol 61, no 06
Ren S, He K, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, vol 28 (NIPS 2015), arXiv:1506.01497v3
Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, vol 29 (NIPS 2016) arXiv:1605.06409v2
Liu W et al (2016) SSD: Single Shot MultiBox Detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9905. Springer, Cham
Lin T-Y, Goyal P, Girshick R He, K, Dollár P (2018) Focal loss for dense object detection, arXiv:1708.02002v2
Lin T-Y et al (2017) Feature pyramid networks for object detection, arXiv:1612.03144v2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mrutyunjay, A., Kondrakunta, P., Rallapalli, H. (2020). Non-max Suppression for Real-Time Human Localization in Long Wavelength Infrared Region. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-24318-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24317-3
Online ISBN: 978-3-030-24318-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)