Detección de peatones con variaciones de forma al caminar con Modelos de Forma Activa

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Juan Alberto Antonio
Marcelo Romero


Se provee un detector de peatones con el algoritmo modelos de forma activa (ASM), con las etapas entrenamiento (PDM) y ajuste (ASM). Con PDM, se marcan 50 landmarks y se extraen los perfiles de grises en la silueta de cada peatón en 137 imágenes (peatón 1 y peatón 2) aplicando los modos de variación (PCA). El aporte de este trabajo es el ajuste y detección de un peatón a pesar de las variaciones. Al final los resultados evaluados con leave one out en cada imagen de 1 080 × 720 pixeles y con la métrica del error cuadrático medio (MSE) se obtiene un promedio total de 12.7 pixeles en la distancia de error entre los landmarks originales y los landmarks estimados. 

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ANTONIO, Juan Alberto; ROMERO, Marcelo. Detección de peatones con variaciones de forma al caminar con Modelos de Forma Activa. CIENCIA ergo-sum, [S.l.], v. 27, n. 3, nov. 2020. ISSN 2395-8782. Disponible en: <>. Fecha de acceso: 19 ene. 2022 doi:
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Angonese, A. T., & Ferreira Rosa, P. F. (2017). Multiple people detection and identification system integrated with a dynamic simultaneous localization and mapping system for an autonomous mobile robotic platform. ICMT 2017-6th International Conference on Military Technologies, 779-786.

Arai, K., & Andrie, R. (2012). Gait recognition method based on wavelet transformation and its evaluation with Chinese Academy of Sciences (CASIA) gait database as a human gait recognition dataset. Proceedings of the 9th International Conference on Information Technology, ITNG 2012.

Baumberg, A. M., & Hogg, D. C. (1994). An efficient method for contour tracking using active shape models. Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

Blake, A., Curwen, R. y Zisserman, A. (1993). A framework for spatiotemporal control in the tracking of visual contours. International Journal of Computer Vision.

Cootes, T. F., & Taylor, C. J. (1992). Active Shape Models-‘Smart Snakes’. BMVC92.

Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models-their training and application. Computer Vision and Image Understanding.

Das Choudhury, S., & Tjahjadi, T. (2013). Gait recognition based on shape and motion analysis of silhouette contours. Computer Vision and Image Understanding.

Dollár, P., Wojek, C., Schiele, B. y Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Enzweiler, M., & Gavrila, D. M. (2009). Monocular pedestrian detection: Survey and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Fang, F., Qian, K., Zhou, B., & Ma, X. (2017). Real-Time RGB-D based People Detection and Tracking for Mobile. Proceedings of 2017 IEEE International Conference on Mechatronics and Automation, 1937-1941.

Flohr, F., & Gavrila, D. (2013). PedCut: An iterative framework for pedestrian segmentation combining shape models and multiple data cues. Procedings of the British Machine Vision Conference 2013.

Godil, A. (2007). Advanced human body and head shape representation and analysis. Digital Human Modeling, 92-100.

Halidou, A., You, X., Hamidine, M., Etoundi, R. A., Diakite, L. H., & Souleimanou. (2014). Fast pedestrian detection based on region of interest and multi-block local binary pattern descriptors. Computers and Electrical Engineering.

Hilario, C., Collado, J. M., Armingol, J. M., & De La Escalera, A. (2005). Pedestrian detection for intelligent vehicles based on active contour models and stereo vision. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

Hill, A., Thornham, A., & Taylor, C. J. (2013). Model-Based Interpretation of 3D Medical Images.

Huysmans, T., Moens, P., & Van Audekercke, R. (2005). An active shape model for the reconstruction of scoliotic deformities from back shape data. Clinical Biomechanics.

Ide, I. (2013). Segmentation of Human Instances Using Grab-cut and Active Shape Model Feedback. Computer Science, 11-14.

Jordão, A. y Schwartz, W. R. (2016). The Good, The Fast and The Better Pedestrian Detector. Universidade Federal de Minas Gerais-Departamento de Ciência da Computação, 1, 1-51. Retrieved from

Jung, C. J. (2008). Human Pose Estimation ASM. Retrieved from

Kim, D., Lee, S., & Paik, J. (2009). Active shape model-based gait recognition using infrared images. Communications in Computer and Information Science, 61(4), 275-281.

Kim, D. S., & Lee, K. H. (2013). Segment-based region of interest generation for pedestrian detection in far-infrared images. Infrared Physics & Technology.

Koschan, A., Kang, S., Paik, J., Abidi, B., & Abidi, M. (2003). Color active shape models for tracking non-rigid objects. Pattern Recognition Letters.

Lakshmi, A., Faheema, A. G. J., & Deodhare, D. (2016). Pedestrian detection in thermal images: An automated scale based region extraction with curvelet space validation. Infrared Physics & Technology.

Le, V., Brandt, J., Lin, Z., Bourdev, L., & Huang, T. S. (2012). Interactive facial feature localization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

Lee, D., & Choi, S. (2011). Multisensor fusion-Based object detection and tracking using Active Shape Model. 2011 6th International Conference on Digital Information Management 2011, 108-114.

Ma, J., & Ren, F. (2011). Detect and track the dynamic deformation human body with the active shape model modified by motion vectors. 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, 587-591.

Müller, J., & Arens, M. (2010). Human pose estimation with Implicit Shape Models. ARTEMIS’10-Proceedings of the 1st ACM Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, Co-located with ACM Multimedia 2010.

Ogawara, K., Li, X., & Ikeuchi, K. (2007). Marker-less human motion estimation using articulated deformable model. Proceedings. IEEE International Conference on Robotics and Automation.

Pentland, A., & Horowitz, B. (1991). Recovery of Non-Rigid Motion and Structure. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Razali, N., & Wahab, A. (2011). 2D Affective Space Model (ASM) for detecting autistic children. Proceedings of the International Symposium on Consumer Electronics.

Ressler, S. (2001). A Web-based 3D Glossary for Anthropometric Landmarks. Proceedings of HCI International, 1, 1-5.

Sadoghi Yazdi, H., Fariman, H. J., & Roohi, J. (2012). Gait recognition based on invariant leg classification using a neuro-fuzzy algorithm as the fusion method. ISRN Artificial Intelligence.

Scott, I. M., Cootes, T. F., &Taylor, C. J. (2003). Improving appearance model matching using local image structure. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

Vandenbroucke, N., Macaire, L., Vieren, C., & Postaire, J. G. (1997). Contribution of a color classification to soccer players tracking with snakes. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.

Vasconcelos, M. J. M., & Tavares, J. M. R. S. (2015). Human motion segmentation using active shape models. Lecture Notes in Computational Vision and Biomechanics.

Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing.

Zhang, S., Bauckhage, C., & Cremers, A. B. (2015). Efficient pedestrian detection via rectangular features based on a statistical shape model. IEEE Transactions on Intelligent Transportation Systems.