Alba Fernández

Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios

Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenarios. Two common approaches are often used, one based on low-level techniques to detect each object size, position and velocity, and other based on high-level techniques that deal with object appearance. None of these methods can deal with all possible problems in multiple-target tracking: environment occlusions, both total and partial, and collisions, such as grouping and splitting events. So one solution is to merge these techniques to improve their performance. Based on an existing hierarchical architecture, we present a novel technique that can deal with all the mentioned problems in multiple tracking targets. Blob detection, low-level tracking using adaptive filters, high-level tracking based on a fixed pool of histograms and an event management that can detect every collision event and performs occlusion recovery are used to be able to track every object during the time they appear within the scene. Experimental results show the performance of this technique under multiple situations, being able to track every object in the scene without losing their initial identification. The speed processing is higher than 50 frames, which allows it to be used under real-time scenarios.

Contextual and skin color region information for face and arms location

Interest in intelligent human-computer interfaces has grown in recent years due to the possibilities that they offer. To these systems, two of the most important sources of interaction are the face and the arms gestures. Different face detection approaches have been made up to date, while arms detection is still a challenging task. This paper describes a methodology for the location of faces and arms in color images combining color information with region information and domain knowledge information. The obtained method is able to work very accurately regardless of races and skin colors, poses, resolutions, lighting conditions, and so on. It has been tested with a representative range of different arm positions, achieving encouraging results.

Automatic Eye Gesture Recognition in Audiometries for Patients with Cognitive Decline

This paper provides a specifically adapted methodology for supporting the audiologists when testing the hearing of patients with cognitive decline or other communication disabilities. These patients can not interact with the audiologist conventionally, but they often express gestural reactions when they perceive the auditory stimuli typically associated to the eyes region. From a video sequence captured during the hearing evaluation, we analyze the movements in the area of the patient’s eyes, so we can detect these gestural reactions. We define a set of different gestures for classification, based on the expert knowledge. The proposed method achieves an accuracy of the 90.65% when classifying these movements, showing their separability, and therefore, the possibility of interpreting them with high-level information as positive reactions to the auditory stimuli.

A Methodology for the Analysis of Spontaneous Reactions in Automated Hearing Assessment

Audiology is the science of hearing and auditory processes study. The evaluation of hearing capacity is commonly performed by an audiologist using an audiometer, where the patient is asked to show some kind of sign when he or she recognizes the stimulus. This evaluation becomes much more complicated when the patient suffers some type of cognitive decline that hinders the emission of visible signs of recognition. With this group of patients, a typical question-answer interaction is not applicable, so the audiologist must focus his attention on the patients spontaneous gestural reactions. This manual evaluation entails a number of problems: it is highly subjective, difficult to determine in real time (since the expert must pay attention simultaneously to the audiological process and the patients reactions), etc. Considering this, in this paper, we present an automatic methodology for processing video sequences recorded during the performance of the hearing test in order to assist the audiologist in the detection of these spontaneous reactions. This screening method analyzes the movements that occur within the eye area, which has been pointed out by the audiologists as the most representative for these patients. By the analysis of these movements, the system helps the audiologist to determine when a positive gestural reaction has taken place increasing the objectivity and reproducibility.

Detection and Characterization of the Sclera - Evaluation of Eye Gestural Reactions to Auditory Stimuli

Hearing assessment becomes a challenge for the audiologists when there are severe difficulties in the communication with the patient. This methodology is aimed at facilitating the audiological evaluation of the patient when cognitive decline, or other communication disorders, complicate the necessary interaction between patient and audiologist for the proper development of the test. In these cases, the audiologist must focus his attention on the detection of spontaneous and unconscious reactions that tend to occur in the eye region of the patient, expressed in most cases as changes in the gaze direction. In this paper, the tracking of the gaze direction is addressed by the study of the sclera, the white area of the eye. The movement is identified and characterized in order to determine whether or not a positive reaction to the auditory stimuli has occurred, so the hearing of the patients can be correctly assessed.

Automatic processing of audiometry sequences for objective screening of hearing loss

Audiology is the branch of science that deals with hearing, balance, and related disorders. Detecting patients with slow responses to auditory stimuli is relevant because this slowness could be due to other cognitive problems or conditions, which should be studied carefully. In this paper, we present an automatic methodology for processing video sequences recorded during the performance of hearing tests to patients. This screening method allows us to measure the patients response times to the auditory stimuli sent to them, and based on these times, to identify those patients with response times abnormally slow. The method is tested on individuals taken at random from a standard population, and based on the obtained results, it is confirmed that the proposed method is valid for the automatic detection of patients with slow response times, and it also serves to the experts as a tool for the accurate and objective measurement of these times.

Path analysis in multiple-target video sequences

Path analysis becomes a powerful tool when dealing with behavior analysis, i. e., detecting abnormal movements. In a multiple target scenario it is complicated to obtain each object path because of collision events, such as grouping and splitting targets, and occlusions, both total or partial. In this work, a method to obtain the similarity between different trajectories is presented, based in register techniques. In addition, an hierarchical architecture is used to obtain the corresponding paths of the objects in a scene, to cope with collision events. Experimental results show promising results in path analysis, enabling it to establish thresholds to abnormal path detection.

Automatic Analysis of the Patient's Conscious Responses to the Emission of Auditory Stimuli during the Performance of an Audiometry

This paper presents a methodology for the automatic detection of human conscious responses to different auditory stimuli during the performance of an audiogram. An audiogram is the standard way of analyze the hearing of a patient in order to diagnose hearing loss. From a video sequence captured during the hearing evaluation performed by an expert to a patient, the methodology will be able to detect the instants in which the expert is sending auditory stimuli and when the patient responds consciously to them by raising his hand in order to indicate that he has heard them. The proposed method has been tested on a dataset of almost 7000 frames from three different video sequences obtaining very encouraging results in the hand raise detection as a response to the auditory stimuli. Finally, an analysis of the patients speed reaction to stimuli is discussed.

Optimization of Topological Active Models with Multiobjective Evolutionary Algorithms

In this work we use the evolutionary multiobjective methodology for the optimization of topological active models, a deformable model that integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by energy functions that must be minimized. As in other deformable models, a correct segmentation is achieved through the optimization of the model, governed by energy parameters that must be experimentally tuned. Evolutionary multiobjective optimization gives a solution to this problem by considering the optimization of several objectives in parallel. Concretely, we use the SPEA2 algorithm, adapted to our application, the search of the Pareto optimal individuals. The proposed method was tested on several representative images from different domains yielding highly accurate results.

Trajectory Similarity Measures Using Minimal Paths

Dealing with surveillance systems, large amount of distance measures are presented in order to classify both normal and abnormal behavior. Typically, techniques based in point-to-point distances are used. However, these techniques do not take into account information about the environment, like pits or restricted areas, for instance. Using a minimal path algorithm to model the usual paths, we develop new trajectory distance measures that are able to introduce information about the scene. The results obtained show promising results.