Abstract
In this work we contribute to development of a “Human-like Visual-Attention-based Artificial Vision” system for boosting firefighters’ awareness about the hostile environment in which they are supposed to move along. Taking advantage from artificial visual-attention, the investigated system’s conduct may be adapted to firefighter’s way of gazing by acquiring some kind of human-like artificial visual neatness supporting firefighters in interventional conditions’ evaluation or in their appraisal of the rescue conditions of people in distress dying out within the disaster. We achieve such a challenging goal by combining a statistically-founded bio-inspired saliency detection model with a Machine-Learning-based human-eye-fixation model. Hybridization of the two above-mentioned models leads to a system able to tune its parameters in order to fit human-like gazing of the inspected environment. It opens appealing perspectives in computer-aided firefighters’ assistance boosting their awareness about the hostile environment in which they are supposed to evolve. Using as well various available wildland fires images’ databases as an implementation of the investigated concept on a 6-wheeled mobile robot equipped with communication facilities, we provide experimental results showing the plausibility as well as the efficiency of the proposed system.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
FAO (2007) Wildfire management, a burning issue for livelihoods and land-use. Available online: http://www.fao.org/newsroom/en/news/2007/1000570/index.html
San-Miguel-Ayanz J, Ravail N, Kelha V, Ollero A (2005) Active fire detection for emergency management: potential and limitations for the operational use of remote sensing. Nat Hazards 35:361–76
Kukreti SR, Kumar M, Cohen K (2016) Detection and localization using unmanned aerial systems for firefighting applications, AIAA Infotech Aerospace, AIAA SciTech, AIAA 2016–1903
Lu G, Yan Y, Huang Y, Reed A (1999) An intelligent monitoring and control system of combustion flames. Meas Control 32(7):164–68
Chen T, Wu P, Chiou Y (2004) An early fire-detection method based on image processing. In: Proceedings of International Conference on Image Processing, pp 1707–1710
Gilabert G, Lu G, Yan Y (2007) Three-dimensional tomographic renconstruction of the luminosity distribution of a combustion flame. IEEE Trans Instr Measure 56(4):1300–1306
Toulouse T, Rossi L, Celik T, Akhloufi M, Maldague X (2015) Benchmarking of wildland fire color segmentation algorithms. IET Image Process 9(12):1064–1072
Ko BC, Cheong KH, Nam JY (2009) Fire detection based on vision sensor and support vector machines. Fire Saf J 44:322–329
Celik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44:147–158
Ho C-C (2009) Machine vision-based real-time early flame and smoke detection. Meas Sci Technol 20(4):045502. https://doi.org/10.1088/0957-0233/20/4/045502
Du S-Y, Liu Z-G (2015) A comparative study of different color spaces in computer-vision-based flame detection. Multimed Tools Appl 75:1–20
Koerich Borges PV, Izquierdo E (2010) A probabilistic approach for vision-based fire detection in videos. IEEE Trans Circuits Syst Video Technol 20(5):721–731
Wang DC, Cui X, Park E, Jin C, Kim H (2013) Adaptive flame detection using randomness testing and robust features. Fire Saf J 55:116–125
Wald A, Wolfowitz J (1943) An exact test for randomness in the non-parametric case based on serial correlation. Ann Math Stat 14(4):378–388
Rossi L, Akhloufi M, Tison Y, Pieri A (2011) On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics. Fire Saf J 46(1-2):9–20
Ko B, Jung JH, Nam JY (2014) Fire detection and 3D surface reconstruction based on stereoscopic pictures and probabilistic fuzzy logic. Fire Saf J 68:61–70
Thokale A, Sonar P (2015) Hybrid approach to detect a fire based on motion color and edge. Digital Image Process 7(9):273–277
Kong SG, Jin D, Li S, Kim H (2015) Fast fire flame detection in surveillance video using logistic regression and temporal smoothing. Fire Saf J 79:37–43
Liu Z-G, Yang Y, Ji X-H, (2016) Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space. SIViP 10(2):277–284
Combination of Experts Based on Color (2015) Shape, and Motion. IEEE Trans Circuits Syst Video Technol 25(9):1545–1556
Kleinbaum DG, Klein M (1994) Logistic regression: a self-learning text. Springer, Berlin. ISBN 978-1-4419-1741-6
Bulas-Cruz J, Ali AT, Dagless EL (1993) A temporal smoothing technique for real-time motion detection. Computer Analysis of Images and Patterns. Springer, Berlin, pp 379–386
Brand RJ, Baldwin DA, Ashburn LA (2002) Evidence for ’motionese’: modifications in mothers infant-directed action. Dev Sci 5:72–83
Wolfe JM, Horowitz TS (2004) What attributes guide the deployment of visual attention and how do they do it? Nat Rev Neurosci 5:495–501
Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: Proceedings of IEEE international conference on computer vision and pattern recognition
Itti L, Koch C, Niebur E (1998) A Model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259
Harel J, Koch C, Perona P (2007) Graph-based visual saliency. Adv Neural Inf Proces Syst 19:545–552
Achanta R, Estrada F, Wils P, Susstrunk S (2008) Salient region detection and segmentation. In: Proceedings of international conference on computer vision systems, vol 5008. LNCS, Springer, Berlin / Heidelberg, pp 66–75
Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum H-Y. (2001) Learning to Detect a Salient Object. IEEE Trans Pattern Anal Mach Intell 33(2):353–367
Liang Z, Chi Z, Fu H, Feng D (2012) Salient object detection using content-sensitive hypergraph representation and partitioning. Pattern Recogn 45(11):3886–3901
Ramík DM, Sabourin C, Madani K (2011) Hybrid salient object extraction approach with automatic estimation of visual attention scale. In: Proceedings of 7th International Conference on Signal Image Technology & Internet-Based Systems. Dijon, France, pp 438–445
Ramík DM, Sabourin C, Moreno R, Madani K (2014) A Machine Learning based Intelligent Vision System for Autonomous Object Detection and Recognition. J Appl Intelligence 40(2):358–375
Moreno R, Ramík DM, Graña M, Madani K (2012) Image segmentation on the spherical coordinate representation of the RGB color space. IET Image Process 6(9):1275–1283
Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum H-Y (2011) Learning to detect a salient object. Proc Comput Vision Pattern Recogn 33:353–367
Borji A, Itti L (2013) State-of-the-art in visual attention modeling. IEEE Trans Pattern Anal Mach Intell 35(1):185–207
Liu T, Sun J, Zheng N, Shum H-Y (2007) Learning to detect a salient object. In: Proceedings IEEE ICCV, pp 1–8
Holzbach A, Cheng G (2014) A Scalable and efficient method for salient region detection using sampled template collation. In: Proceedings IEEE ICIP, pp 1110–1114
Koehler K, Guo F, Zhang S, Eckstein MP (2014) What do saliency models predict. J Vis 14(3):1–27
Navalpakkam V, Itti L (2006) An integrated model of top-down and bottom-up attention for optimizing detection speed. Proc IEEE CVPR II:2049–2056
Kadir T, Brady M (2001) Saliency, scale and image description. J Vis 45(2):83–105
Kienzle W, Franz MO, Schölkopf B, Wchmann FA (2009) Center-surround patterns emerge as optimal predictors for human saccade targets. J Vis 9:1–15
Rajashekar U, Vander Linde I, Bovik AC, Cormack LK (2008) GAFFE: a gaze- attentive fixation finding engine. IEEE Trans Image Process 17(4):564–573
Hayhoe M, Ballard D (2005) Eye movements in natural behavior. Trends Cogn Sci 9:188–194
Triesch J, Ballard DH, Hayhoe MM, Sullivan BT (2003) What you see is what you need. J Vis 3:86–94
Zhang J, Sclaroff S (2013) Saliency detection: a boolean map approach. In: Proceedings of IEEE ICCV, pp 153–160
Karray FO, De Silva CW (2004) Soft computing and intelligent systems design: theory, tools and applications. Addison-Wesley Longman, Harlow. ISBN 9780321116178
Riche N, Duvinage M, Mancas M, Gosselin B, Dutoit T (2013) Saliency and human fixations: state-of-the-art and study of comparison metrics. In: Procssdings of IEEE ICCV, pp 1153–1160
Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: Proceedings of IEEE ICCV, pp 2106–2113
Borji A, Tavakoli HR, Sihite DN, Itti L (2013) Analysis of scores, datasets, and models in visual saliency prediction. In: Proceedings of IEEE ICCV, pp 921–928
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874
Contreras-Reyes JE, Arellano-Valle RB (2012) Küllback-Leibler divergence measure for multivariate skew-normal distributions. Entropy 14(9):1606–1626
Judd T, Durand F, Torralba A (2012) A benchmark of computational models of saliency to predict human fixations, MIT Technical Report. http://saliency.mit.edu/
Tatler BW (2007) The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor bases and image feature distributions. J V 14:1–17
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Madani, K., Kachurka, V., Sabourin, C. et al. A human-like visual-attention-based artificial vision system for wildland firefighting assistance. Appl Intell 48, 2157–2179 (2018). https://doi.org/10.1007/s10489-017-1053-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-017-1053-6