People counting using a consumer RGBD camera

[EN] In this paper, we prove that depth information provided by a consumer depth camera is a reliable data source to perform ro- bust people counting. The adoption of a top view configuration reduces the space problem complexity for this task, while pre- serving privacy. Two different background subtraction approaches for color images are transferred to this context and tested in real video to perform detection, tracking, and behavioral pat- terns analysis of subjects crossing the field of view. The results achieved in an experimental setup with real video reported a TPR over 95%, beating robust GMM background subtraction based only on the visual cue. The results suggest the benefits of the depth cue for this particular task.[ES] En este trabajo se demuestra que la información de profundidad proporcionada por una cámara RGBD comercial de bajo coste, es una fuente fiable de datos para realizar de forma robusta el conteo automático de personas. La adopción de una configuración de vista cenital reduce la complejidad del problema, al mismo tiempo que permite preservar la privacidad de las personas moni- torizadas. Para llevar a cabo el estudio experimental se han considerado dos técnicas propias del campo de análisis de imágenes 2D trasladadas al contexto de imágenes de profundidad. Las pruebas evaluaron su rendimiento con v́ıdeos reales sin restricciones de iluminación, incluyendo episodios de iluminación cambiante o muy baja. En este conjunto experimental se realizó la detección, seguimiento y análisis de patrones de comportamiento de las personas que cruzaban el campo de visión. Los resultados obtenidos alcanzan una tasa de acierto próxima al 95%, superando los obtenidos con técnicas actuales basadas exclusivamente en información visual. Estos resultados sugieren la utilidad del uso de información de profundidad en esta tarea particular.Trabajo parcialmente apoyado por el Departamento de Informatica ´ y Sistemas de la ULPGC.Castrillón Santana, M.; Lorenzo Navarro, J.; Hernández Sosa, D. (2014). Conteo de personas con un sensor RGBD comercial. Revista Iberoamericana de Automática e Informática industrial. 11(3):348-357. https://doi.org/10.1016/j.riai.2014.05.006OJS34835711

Cue Combination for Robust Real-Time Multiple Face Detection at Different Resolutions

The face detection problem, defined as: to determine any face-if any- in the image returning the location and extent of each [Yang et al., 2002], seems to be solved, according to some recent works [Schneiderman and Kanade, 2000] [Viola and Jones, 2001]. Particularly for video stream processing, these approache

Towards Real-Time Multiresolution Face/Head Detection

Reliable and real-time face detection is a basic ability for any Vision Based Interface. This paper combines and exploits the bene¯ts of two di®erent face detectors specialized each one in a speci¯c context. The resulting system improves their respective individual performances by means of their cooperation, the integration of temporal coherence, persistence and explicit knowledge about the human face, achieving a robust and close to real-time multiresolution face detector

FACE AND FACIAL FEATURE DETECTION EVALUATION Performance Evaluation of Public Domain Haar Detectors for Face and Facial Feature Detection

Abstract: Fast and reliable face and facial feature detection are required abilities for any Human Computer Interaction approach based on Computer Vision. Since the publication of the Viola-Jones object detection framework and the more recent open source implementation, an increasing number of applications have appeared, particularly in the context of facial processing. In this respect, the OpenCV community shares a collection of public domain classifiers for this scenario. However, as far as we know these classifiers have never been evaluated and/or compared. In this paper we analyze the individual performance of all those public classifiers getting the best performance for each target. These results are valid to define a baseline for future approaches. Additionally we propose a simple hierarchical combination of those classifiers to increase the facial feature detection rate while reducing the face false detection rate.

Ear Recognition with Neural Networks Based on Fisher and Surf Algorithms

This paper offers an approach to biometric analysis using ears for recognition. The ear has all the assets that a biometric trait should possess. Because it is a study field in potential growth, this research offers an approach using Speeded Up Robust Features (SURF) and Fisher Linear Discriminant Analysis (LDA) as an input of two neural networks with the purpose to detect and recognize a person by the patterns of its ear. It also includes the development of an application with .net to show experimental results of the applied theory. In the preprocessing task, the system adds sturdiness using Hausdorff distance to increase the performance filtering for the subjects to use in the testing stage of the neural network. To perform this study, we worked with the help of Ávila’s police school (Spain), where we built a database with approximately 300 ears. The investigation results shown that the integration of LDA and SURF in neural networks can improve the ear recognition process and provide robustness in changes of illumination and perception