Javier Martínez-Cantos

Bio: Javier Martínez-Cantos is an academic researcher from ETSI. The author has contributed to research in topics: Background subtraction & Segmentation. The author has an hindex of 4, co-authored 7 publications receiving 206 citations. Previous affiliations of Javier Martínez-Cantos include National University of Distance Education & University of Castilla–La Mancha.

Optical flow or image subtraction in human detection from infrared camera on mobile robot

Abstract: Perceiving the environment is crucial in any application related to mobile robotics research. In this paper, a new approach to real-time human detection through processing video captured by a thermal infrared camera mounted on the autonomous mobile platform mSecurit^T^M is introduced. The approach starts with a phase of static analysis for the detection of human candidates through some classical image processing techniques such as image normalization and thresholding. Then, the proposal starts a dynamic image analysis phase based in optical flow or image difference. Optical flow is used when the robot is moving, whilst image difference is the preferred method when the mobile platform is still. The results of both phases are compared to enhance the human segmentation by infrared camera. Indeed, optical flow or image difference will emphasize the foreground hot spot areas obtained at the initial human candidates' detection.

A new video segmentation method of moving objects based on blob-level knowledge

Abstract: Variants of the background subtraction method are broadly used for the detection of moving objects in video sequences in different applications. In this work we propose a new approach to the background subtraction method which operates in the colour space and manages the colour information in the segmentation process to detect and eliminate noise. This new method is combined with blob-level knowledge associated with different types of blobs that may appear in the foreground. The idea is to process each pixel differently according to the category to which it belongs: real moving objects, shadows, ghosts, reflections, fluctuation or background noise. Thus, the foreground resulting from processing each image frame is refined selectively, applying at each instant the appropriate operator according to the type of noise blob we wish to eliminate. The approach proposed is adaptive, because it allows both the background model and threshold model to be updated. On the one hand, the results obtained confirm the robustness of the method proposed in a wide range of different sequences and, on the other hand, these results underline the importance of handling three colour components in the segmentation process rather than just the one grey-level component.

On the effect of feedback in multilevel representation spaces for visual surveillance tasks

Abstract: In this work we propose a general top-down feedback scheme between adjacent description levels to interpret video sequences. This scheme distinguishes two types of feedback: repair-oriented feedback and focus-oriented feedback. With the first it is possible to improve the system's performance and produce more reliable and consistent information, and with the second it is possible to adjust the computational load to match the aims. Finally, the general feedback scheme is used in different examples for a visual surveillance application which improved the final result of each description level by using the information in the higher adjacent level.

Parametric improvement of lateral interaction in accumulative computation in motion-based segmentation

Abstract: Segmentation of moving objects is an essential component of any vision system. However, its accomplishment is hard due to some challenges such as the occlusion treatment or the detection of objects with deformable appearance. In this paper an artificial neuronal network approach for moving object segmentation, called lateral interaction in accumulative computation (LIAC), which uses accumulative computation and recurrent lateral interaction is revisited. Although the results reported for this approach so far may be considered relevant, the problems faced each time (environment, objects of interest, etc.) make that the system outcome varies. Hence, our aim is to improve segmentation provided by LIAC in a double sense: by removing the detected objects not matching some size or compactness constraints, and by learning suitable parameters that improve the segmentation behavior through a genetic algorithm.

Posprocesamiento morfológico adaptativo basado en algoritmos genéticos y orientado a la detección robusta de humanos

Abstract: Resumen. Existen distintas aproximaciones para la deteccion de objetos moviles basadas en el denominado metodo de background. En secuencias reales, el gran inconveniente de este metodo, que comparte con otros metodos de segmentacion, es la forma de eliminar el ruido inherente tanto al foreground como al background. Una aproximacion muy utilizada para resolver este problema es la aplicacion de una secuencia fija de operadores morfologicos pero que, al tenerse que decidirse a priori, no siempre esta garantizado el exito de la restauracion del foreground. En este trabajo, se propone un metodo de posprocesamiento que, para cada frame, ofrece automaticamente una secuencia de operadores morfologicos obtenida a partir de la salida de un algoritmo genetico cuya busqueda esta fuertemente sesgada hacia la restauracion de siluetas humanas. Finalmente, se propone el uso de este metodo dentro de un sistema de deteccion robusta de humanos.

Thermal cameras and applications: a survey

Abstract: Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras. This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

Multimodal behavioral analysis for non-invasive stress detection

Abstract: This study presents and approach to measure the levels of acute stress in humans by analysing their behavioral patterns when interacting with technological devices. We study the effects of stress on eight behavioral, physical and cognitive features. The data was collected with the participation of 19 users in different phases, with different levels of stress induced. A non-parametric statistical hypothesis test is used to determine which features show statistically significant differences, for each user, when under stress. It is shown that the features more related to stress are the acceleration and the mean and maximum intensity of the touch. It is also shown that each user is affected by stress in a specific way. Moreover, all the process of estimating stress is undertaken in a non-invasive way. This work constitutes the foundation of a context layer for a virtual environment for conflict resolution. The main objective is to overcome some of the main drawbacks of communicating online, namely the lack of contextual information such as body language or gestures.

Sensor-driven agenda for intelligent home care of the elderly

Abstract: The exponential increase of home-bound persons who live alone and are in need of continuous monitoring requires new solutions to current problems. Most of these cases present illnesses such as motor or psychological disabilities that deprive of a normal living. Common events such as forgetfulness or falls are quite common and have to be prevented or dealt with. This paper introduces a platform to guide and assist these persons (mostly elderly people) by providing multisensory monitoring and intelligent assistance. The platform operates at three levels. The lower level, denominated ''Data acquisition and processing'' performs the usual tasks of a monitoring system, collecting and processing data from the sensors for the purpose of detecting and tracking humans. The aim is to identify their activities in an intermediate level called ''activity detection''. The upper level, ''Scheduling and decision-making'', consists of a scheduler which provides warnings, schedules events in an intelligent manner and serves as an interface to the rest of the platform. The idea is to use mobile and static sensors performing constant monitoring of the user and his/her environment, providing a safe environment and an immediate response to severe problems. A case study on elderly fall detection in a nursery home bedroom demonstrates the usefulness of the proposal.

Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Abstract: Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.

Hierarchical Method for Foreground Detection Using Codebook Model

Abstract: This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.