Abstract
In video surveillance, we can rely on either a visible spectrum or an infrared one. In order to profit from both of them, several fusion methods were proposed in literature: low-level fusion, middle-level fusion and high-level fusion. The first one is the most used for moving objects’ detection. It consists in merging information from visible image and infrared one into a new synthetic image to detect objects. However, the fusion process may not preserve all relevant information. In addition, perfect correlation between the two spectrums is needed. In This paper, we propose an intelligent fusion method for moving object detection. The proposed method relies on one of the two given spectrum at once according to weather conditions (darkness, sunny days, fog, snow, etc.). Thus, we first extract a set of low-level features (visibility, local contrast, sharpness, hue, saturation and value), then a prediction model is generated by supervised learning techniques. The classification results on 15 sequences with different weather conditions indicate the effectiveness of the extracted features, by using C4.5 as classifier.
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Brutzer, S., Höferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, pp. 1937–1944 (2011)
Biswas, S., Sil, J., Sengupta, N.: Background modeling and implementation using discrete wavelet transform, a review. International Journal on Graphics, Vision and Image Processing 11(1), 29–42 (2011)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: A comprehensive review. EURASIP J. Adv. Sig. Proc. (2010)
Roth, S., Black, M.J.: On the spatial statistics of optical flow. In: 10th IEEE International Conference on Computer Vision, Beijing, China, pp. 42–49 (2005)
Pathirana, P.N., Lim, A.E.K., Carminati, J., Premaratne, M.: Simultaneous estimation of optical flow and object state: A modified approach to optical flow calculation. In: IEEE International Conference on Networking, Sensing and Control, London, pp. 634–638 (2007)
Lim, S., Apostolopoulos, J.G., Gamal, A.E.: Optical flow estimation using temporally over-sampled video. IEEE Transactions on Image Processing, 1074–1087 (2005)
Guo, J., Chng, E.S., Deepu, R.: Foreground motion detection by difference-based spatial temporal entropy image. In: IEEE Region 10 Conference, pp. 379–382 (2004)
Chang, M., Cheng, Y.: Motion Detection by Using Entropy Image and Adaptive State-Labeling Technique. In: IEEE International Symposium on Circuits and Systems, New Orleans, pp. 3667–3670 (2007)
Durus, M., Ercil, A.: Robust Vehicle Detection Algorithm. In: 15th IEEE International Conference on Signal Processing and Communications Applications, Siu, pp. 1–4 (2007)
Gilmore, E.T., Ugbome, C., Kim, C.: An IR-based Pedestrian Detection System Imple-mented with Matlab-Equipped Laptop and Low-Cost Microcontroller. International Journal of Computer Science & Information Technology 3(5), 79–87 (2011)
Xu, F., Liu, X., Fujimura, K.: Pedestrian Detection and Tracking with Night Vision. IEEE Transactions on Intelligent Transportation Systems 6(5), 63–71 (2005)
Fang, Y., Yamada, K., Ninomiya, Y., Horn, B.K.P., Masaki, I.: A shape-independent method for pedestrian detection with far-infrared images. IEEE Transactions on Vehicular Technology 53(6), 1679–1697 (2004)
Olmeda, D., Hilario, C., de la Escalera, A., Armingol, J.M.: Pedestrian Detection and Tracking Based on Far Infrared Visual Information. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 958–969. Springer, Heidelberg (2008)
Bertozzi, M., Broggi, A., Rose, M.D., Felisa, M., Rakotomamonjy, A., Suard, F.: A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier. In: IEEE Intelligent Transportation Systems Conference, USA, pp. 143–148 (2007)
Nanda, H., Davis, L.: Probabilistic Template Based Pedestrian Detection in Infrared Videos. In: IEEE Intelligent Vehicles Symposium, Paris, France, pp. 15–20 (2002)
Dai, C., Zheng, Y., Li, X.: Pedestrian detection and tracking in infrared imagery using shape and appearance. Journal of Computer Vision and Image Understanding 106, 288–299 (2007)
Zin, T.T., Takahashi, H., Toriu, T., Hama, H.: Fusion of Infrared and Visible Images for Robust Person Detection. Image Fusion (2011) ISBN: 978-953-307-679-9
Goubet, E., Katz, J., Porikli, F.: Pedestrian tracking using thermal infrared imaging. In: Infrared Technology and Applications XXXII Proc. SPIE, vol. 6206 (2006), doi:10.1117/12.673132
Apătean, A.D.: Contributions to the Information Fusion, Application to Obstacle Recognition in Visible and Infrared Images. Doctoral thesis (2011)
Wang, J., Liang, J., Hu, H., Li, Y., Feng, B.: Performance evaluation of infrared and visible image fusion algorithms for face recognition. In: International Conference on Intelligent Systems and Knowledge Engineering, Chengdu, China, pp. 1–8 (2007)
Ó Conaire, C., Cooke, E., O’Connor, N., Murphy, N., Smeaton, A.: Fusion of infrared and visible spectrum video for indoor surveillance. In: 6th International Workshop on Image Analysis for Multimedia Interactive Services, Montreux, Switzerland (2005)
Apatean, A., Rogozan, A., Bensrhair, A.: Information Fusion for Obstacle Recognition in Visible and Infrared Images. In: International Symposium on Signals, Circuits and Systems, pp. 1–4 (2009)
Blum, R.S., Xue, Z., Zhang, Z.: An Overview of Image Fusion. In: Multi-Sensor Image Fusion and Its Applications, pp. 1–36. Taylor & Francis, Boca Raton (2006)
Sadjadi, F.: Comparative Image Fusion Analysais. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, vol. 8(8), p. 25 (2005)
Samadzadegan, F.: Data Integration Related to Sensors, Data and Models. In: International Society for Photogrammetry and Remote Sensing (2004)
Yang, B., Zhong-liang, J., Hai-tao, Z.: Review of pixel-level image fusion. Journal of Shanghai Jiaotong University 15(1), 6–12 (2010)
Roser, M., Moosmann, F.: Classification of Weather Situations on Single Color Images. In: IEEE Intelligent Vehicles Symposium Eindhoven University of Technology, Eindhoven, The Netherlands, pp. 798–803 (2008)
Zhao, X., Liu, P., Liu, J., Tang, X.: Feature extraction for classification of different weather conditions. Frontiers of Electrical and Electronic Engineering 6(2), 339–346 (2011)
Hammami, M., Jarraya, S.K., Ben-Abdallah, H.: On line background modeling for moving object segmentation in dynamic scene. Multimedia Tools and Applications 63(3), 899–926 (2013)
Hammami, M., Ben Romdhane, N., Ben-Abdallah, H.: An Improved Lane Detection and Tracking Method for Lane Departure Warning Systems. International Journal of Computer Vision and Image Processing 3(3), 1–15 (2013)
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Boukhriss, R.R., Fendri, E., Hammami, M. (2015). Intelligent Fusion of Infrared and Visible Spectrum for Video Surveillance Application. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_69
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DOI: https://doi.org/10.1007/978-3-319-19324-3_69
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