Supervised evolutionary learning: Use of gradient histogram & particle swarm algorithm to detection & tracking pedestrian in sequence of infrared images

Karim Zare; Seyedmohammad Shahrokhi; Mohammadreza Amini

doi:10.15649/2346075X.2319

Autores/as

Karim Zare Professor of Chemistry, Islamic Azad University, Science and Research Branch, Tehran, Iran
Seyedmohammad Shahrokhi Department of Electrical Engineering, Borujerd Branch, Islamic Azad University, Borujerd
Mohammadreza Amini Department of Electrical Engineering, Borujerd Branch, Islamic Azad University, Borujerd

DOI:

https://doi.org/10.15649/2346075X.2319

Palabras clave:

Identificación de peatoness, Imágenes infrarrojas, Red neuronal de aprendizaje máximo, Gradient Histogram, Histograma de degradado, Optimización por enjambre de partículas

Resumen

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification. In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response. There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers. Researchers are looking for new processing models that can accurately monitor one's position on the move. In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented. It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images. As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning. The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms.

Referencias

Zou, H., Sun, H., & Ji, K. (2012, December). Real-time infrared pedestrian detection via sparse representation. In Computer Vision in Remote Sensing (CVRS), 2012 International Conference on (pp. 195-198). IEEE.

Wang, J. T., Chen, D. B., Chen, H. Y., & Yang, J. Y. (2012). On pedestrian detection and tracking in infrared videos. Pattern Recognition Letters, 33(6), 775-785.

https://doi.org/10.1016/j.patrec.2011.12.011

Teutsch, M., & Müller, T. (2013, May). Hot spot detection and classification in LWIR videos for person recognition. In SPIE Defense, Security, and Sensing (pp. 87440F- 87440F). International Society for Optics and Photonics.

https://doi.org/10.1117/12.2015754

Elguebaly, T., & Bouguila, N. (2013). Finite asymmetric generalized Gaussian mixture models learning for infrared object detection. Computer Vision and Image Understanding, 117(12), 1659-1671.

https://doi.org/10.1016/j.cviu.2013.07.007

Teutsch, M., Muller, T., Huber, M., & Beyerer, J. (2014). Low resolution person detection with a moving thermal infrared camera by hot spot classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 209-216).

https://doi.org/10.1109/CVPRW.2014.40

Akhloufi, M. A., Porcher, C., & Bendada, A. (2014, June). Fusion of thermal infrared and visible spectrum for robust pedestrian tracking. In SPIE Defense+ Security (pp. 90760O-90760O). International Society for Optics and Photonics.

https://doi.org/10.1117/12.2063306

Soundrapandiyan, R., & Mouli, P. C. (2015). Adaptive Pedestrian Detection in Infrared Images Using Background Subtraction and Local Thresholding. Procedia Computer Science, 58, 706-713.

https://doi.org/10.1016/j.procs.2015.08.091

Rajkumar, S., & Mouli, P. C. (2015, February). Pedestrian detection in infrared images using local thresholding. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on (pp. 259-263). IEEE.

https://doi.org/10.1109/ECS.2015.7124904

Berg, A., Ahlberg, J., & Felsberg, M. (2015, August). A thermal Object Tracking benchmark. In Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on (pp. 1-6). IEEE.

https://doi.org/10.1109/AVSS.2015.7301772

Hwang, S., Park, J., Kim, N., Choi, Y., & So Kweon, I. (2015). Multispectral pedestrian detection: Benchmark dataset and baseline. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1037-1045).

https://doi.org/10.1109/CVPR.2015.7298706

Pawłowski, P., Piniarski, K., & Dąbrowski, A. (2015, September). Pedestrian detection in low resolution night vision images. In Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2015 (pp. 185-190). IEEE.

https://doi.org/10.1109/SPA.2015.7365157

Yang, C., Liu, H., Liao, S., & Wang, S. (2015). Pedestrian Detection in Thermal Infrared Image Using Extreme Learning Machine. In Proceedings of ELM-2014 Volume 2 (pp. 31-40). Springer International Publishing.

https://doi.org/10.1007/978-3-319-14066-7_4

Zhao, X., He, Z., Zhang, S., & Liang, D. (2015). Robust pedestrian detection in thermal infrared imagery using a shape distribution histogram feature and modified sparse representation classification. Pattern Recognition, 48(6), 1947-1960

https://doi.org/10.1016/j.patcog.2014.12.013

Zhuang, J., & Liu, Q. (2016). Transferred IR pedestrian detector toward distinct scenarios adaptation. Neural Computing and Applications, 27(3), 557-569.

https://doi.org/10.1007/s00521-015-1877-0

Ma, Y., Wu, X., Yu, G., Xu, Y., & Wang, Y. (2016). Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery. Sensors, 16(4), 446.

https://doi.org/10.3390/s16040446

Dai, C., Zheng, Y., & Li, X. (2007). Pedestrian detection and tracking in infrared imagery using shape and appearance. Computer Vision and Image Understanding, 106(2), 288-299.

https://doi.org/10.1016/j.cviu.2006.08.009

Seryasat, O. R., Honarvar, F., & Rahmani, A. (2010, October). Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine (MSVM). In 2010 IEEE International Conference on Systems, Man and Cybernetics (pp. 4300-4303). IEEE.

https://doi.org/10.1109/ICSMC.2010.5642390

Seryasat, O. R., & Haddadnia, J. (2018). Evaluation of a new ensemble learning framework for mass classification in mammograms. Clinical breast cancer, 18(3), e407-e420.

https://doi.org/10.1016/j.clbc.2017.05.009

Seryasat, Omid Rahmani, and Javad Haddadnia. "Evaluation of a new ensemble learning framework for mass classification in mammograms." Clinical breast cancer 18.3 (2018): e407-e420.

https://doi.org/10.1016/j.clbc.2017.05.009

Biswas, S. K., & Milanfar, P. (2017). Linear support tensor machine with LSK channels: Pedestrian detection in thermal infrared images. IEEE transactions on image processing, 26(9), 4229-4242.

https://doi.org/10.1109/TIP.2017.2705426

Cai, Y., Liu, Z., Wang, H., & Sun, X. (2017). Saliency-based pedestrian detection in far infrared images. IEEE Access, 5, 5013-5019.

https://doi.org/10.1109/ACCESS.2017.2695721

Ma, M. (2019). Infrared pedestrian detection algorithm based on multimedia image recombination and matrix restoration. Multimedia Tools and Applications, 1-16.

Kwak, J. Y., Ko, B. C., & Nam, J. Y. (2017). Pedestrian tracking using online boosted random ferns learning in far-infrared imagery for safe driving at night. IEEE Transactions on Intelligent Transportation Systems, 18(1), 69-81.

https://doi.org/10.1109/TITS.2016.2569159

Bai, X., Wang, Y., Liu, H., & Guo, S. (2018). Symmetry information based fuzzy clustering for infrared pedestrian segmentation. IEEE Transactions on Fuzzy Systems, 26(4), 1946-1959.

https://doi.org/10.1109/TFUZZ.2017.2756827

Shen, G., Zhu, L., Jihan, L. O. U., Shen, S., Liu, Z., & Tang, L. (2019). Infrared multi-pedestrian tracking in vertical view via Siamese Convolution Network (December 2018). IEEE Access.

https://doi.org/10.1109/ACCESS.2019.2892469

Lahmyed, R., El Ansari, M., & Ellahyani, A. (2018). A new thermal infrared and visible spectrum images-based pedestrian detection system. Multimedia Tools and Applications, 1-25.

https://doi.org/10.1007/s11042-018-6974-5

Hsu, W. Y. (2018). Automatic pedestrian detection in partially occluded single image. Integrated Computer-Aided Engineering, 25(4), 369-379.

https://doi.org/10.3233/ICA-170573

Llorca, D. F., Sotelo, M. A., Hellín, A. M., Orellana, A., Gavilán, M., Daza, I. G., & Lorente, A. G. (2012). Stereo regions-of-interest selection for pedestrian protection: A survey. Transportation research part C: emerging technologies, 25, 226-237.

https://doi.org/10.1016/j.trc.2012.06.006

Armanfard, N., Komeili, M., & Kabir, E. (2012). TED: A texture-edge descriptor for pedestrian detection in video sequences. Pattern Recognition, 45(3), 983-992.

https://doi.org/10.1016/j.patcog.2011.08.010

Negri, P., Goussies, N., & Lotito, P. (2014). Detecting pedestrians on a movement feature space. Pattern recognition, 47(1), 56-71.

https://doi.org/10.1016/j.patcog.2013.05.020

Rezaee, Kh, Haddadnia, J.Delbari, A, Elderly drop monitoring system based on Gaussian hybrid model and body anatomical changes in video images, Journal of Machine Vision and Image Processing,1(2),76-77

Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513-529.

https://doi.org/10.1109/TSMCB.2011.2168604