Spatiotemporal CNN with Pyramid Bottleneck Blocks: Application to eye blinking detection

Eye blink detection is a challenging problem that many researchers are working on because it has the potential to solve many facial analysis tasks, such as face anti-spoofing, driver drowsiness detection, and some health disorders. There have been few attempts to detect blinking in the wild scenario, while most of the work has been done under controlled conditions. Moreover, current learning approaches are designed to process sequences that contain only a single blink ignoring the case of the presence of multiple eye blinks. In this work, we propose a fast framework for eye blink detection and eye blink verification that can effectively extract multiple blinks from image sequences considering several challenges such as lighting changes, variety of poses, and change in appearance. The proposed framework employs fast landmarks detector to extract multiple facial key points including the ones that identify the eye regions. Then, an SVD-based method is proposed to extract the potential eye blinks in a moving time window that is updated with new images every second. Finally, the detected blink candidates are verified using a 2D Pyramidal Bottleneck Block Network (PBBN). We also propose an alternative approach that uses a sequence of frames instead of an image as input and employs a continuous 3D PBBN that follows most of the state-of-the-art approaches schemes. Experimental results show the better performance of the proposed approach compared to the state-of-the-art approaches

Energy balance in Spectral Filter Array camera design

Multispectral imaging permits to capture more spectral information on object surface properties than color imaging. This is useful for machine vision applications. Transmittance spectral filter arrays combined with a solid state sensor form an emerging technology used for snapshot acquisition. In spectral filter arrays technology, the sensitivities of the camera have critical consequences, not only on applications, but also in the viability of the system. We discuss how to balance the energy of each channel in single exposure multispectral imaging

Improving Safety of Level Crossings by Detecting Hazard Situations Using Video Based Processing

International audienceRoad and level crossing safety become a priority issue for the domain of intelligent transportation systems in recent years. This paper presents a video based approach for detecting and evaluating dangerous situations induced by users (pedestrians, vehicle drivers, unattended objects) in level crossing environments. The approach starts by detecting and tracking objects shot in the level crossing area thanks to a video sensor. Then, a Hidden Markov Model is developed in order to recognize ideal trajectories of the detected objects during their tracking. The level of risk for each identified hazard scenario is estimated instantly by using Demptster-Shafer data fusion technique. Three hazard scenarios are tested and evaluated with different real video image sequences: presence of obstacles in the level crossing, presence of stopped vehicles lines, vehicle zigzagging between two closed half-barriers)

Parameter self-tuning schemes for the two phase test sample sparse representation classifier

International audienceSparse Representation Classifier (SRC) and its variants were considered as powerful classifiers in the domains of computer vision and pattern recognition. However, classifying test samples is computationally expensive due to the l1norm minimization problem that should be solved in order to get the sparse code. Therefore, these classifiers could not be the right choice for scenarios requiring fast classification. In order to overcome the expensive computational cost of SRC, a two-phase coding classifier based on classic Regularized Least Square was proposed. This classifier is more efficient than SRC. A significant limitation of this classifier is the fact that the number of the samples that should be handed over to the next coding phase should be specified a priori. This paper overcomes this main limitation and proposes five data-driven schemes allowing an automatic estimation of the optimal size of the local samples. These schemes handle the three cases that are encountered in any learning system: supervised, unsupervised, and semi-supervised. Experiments are conducted on five image datasets. These experiments show that the introduced learning schemes can improve the performance of the two-phase linear coding classifier adopting ad-hoc choices for the number of local samples

Une approche neuronale pour la stéréovision linéaire

Cet article décrit une nouvelle méthode pour la détection d'obstacles à l'avant des véhicules en utilisant la vision linéaire. Nous nous sommes intéressés à résoudre le problème de l'appariement stéréoscopique en utilisant une approche neuronale. Ce problème est ramené, dans un premier temps, à un problème d'optimisation. Une fonction d'énergie, construite pour représenter toutes les contraintes du problème, est ensuite minimisée en utilisant un réseau de neurones de Hopfield

Equisolid Fisheye Stereovision Calibration and Point Cloud Computation

This paper deals with dense 3D point cloud computation of urban environments around a vehicle. The idea is to use two fisheye views to get 3D coordinates of the surrounding scene's points. The first contribution of this paper is the adaptation of an omnidirectional stereovision self-calibration algorithm to an equisolid fisheye projection model. The second contribution is the description of a new epipolar matching based on a scan-circle principle and a dynamic programming technique adapted for fisheye images. The method is validated using both synthetic images for which ground truth is available and real images of an urban scene

New framework for person-independent facial expression recognition combining textural and shape analysis through new feature extraction approach

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A Novel Evidence Based Model for Detecting Dangerous Situations in Level Crossing Environments

International audienceConsidered as a weak point in road and railway infrastructure, level crossings (LC) improvement safety became an important field of academic research and took increasingly railways undertakings concerns. Improving safety of people and road-rail facilities is an essential key element to ensure a good operating of the road and railway transport. For this purpose, road and railway safety professionals from several countries have been focused on providing level crossings as safer as possible. Many actions are planned in order to exchange information and provide experiments for improving the management of level crossing safety and performance. This paper aims to develop a video surveillance system to detect, recognize and evaluate potentially dangerous situations in level crossing environments. First, a set of moving objects are detected and separated using an automatic clustering process coupled to an energy vector comparison strategy. Then, a multi-object tracking algorithm, based on optical flow propagation and Kalman filtering correction with adaptive parameters, is implemented. The next step consists on using a Hidden Markov Model to predict trajectories of the detected objects. Finally, the trajectories are analysed with a particular credibility model to evaluate dangerous situations at level crossings. Real data sets are used to test the effectiveness and robustness of the method. This work is developed within the framework of PANsafer project, supported by the French work program ANR