Ivan Huerta

Detection and removal of chromatic moving shadows in surveillance scenarios

Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.

A deep analysis on age estimation

Two novel methods for age estimation, using simple alignment unlike previous works.Fusing local texture/appearance descriptors improves over complex features like BIF.We propose a deep learning scheme to improve current state-of-the-art.Exhaustive validation over large databases, outperforming previous results in the field. The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search optimization in large databases. Nevertheless, it becomes challenging to estimate age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity and high within-range variance. These difficulties have been recently addressed with complex and strongly hand-crafted descriptors, difficult to replicate and compare. This paper presents two novel approaches: first, a simple yet effective fusion of descriptors based on texture and local appearance; and second, a deep learning scheme for accurate age estimation. These methods have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work.

A Comparative Evaluation of Regression Learning Algorithms for Facial Age Estimation

The problem of automatic age estimation from facial images poses a great number of challenges: uncontrollable environment, insufficient and incomplete training data, strong person-specificity, and high within-range variance, among others. These difficulties have made researchers of the field propose complex and strongly hand-crafted descriptors, which make it difficult to replicate and compare the validity of posterior classification and regression schemes. We present a practical evaluation of four machine learning regression techniques from some of the most representative families in age estimation: kernel techniques, ensemble learning, neural networks, and projection algorithms. Additionally, we propose the use of simple HOG descriptors for robust age estimation, which achieve comparable performance to the state-of-the-art, without requiring piecewise facial alignment through tens of landmarks, nor fine-tuned and specific modeling of facial aging, nor additional demographic annotations such as gender or ethnicity. By using HOG descriptors, we discuss the benefits and drawbacks among the four learning algorithms. The accuracy and generalization of each regression technique is evaluated through cross-validation and cross-database validation over two large databases, MORPH and FRGC.

Exploiting multiple cues in motion segmentation based on background subtraction

This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motion segmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches.

Improving Background Subtraction Based on a Casuistry of Colour-Motion Segmentation Problems

The basis for the high-level interpretation of observed patterns of human motion still relies on motion segmentation. Popular approaches based on background subtraction use colour information to model each pixel during a training period. Nevertheless, a deep analysis on colour segmentation problems demonstrates that colour segmentation is not enough to detect all foreground objects in the image, for instance when there is a lack of colour necessary to build the background model. In this paper, our segmentation procedure is based not only on colour, but also on intensity information. Consequently, the intensity model enhances segmentation when the use of colour is not feasible. Experimental results demonstrate the feasibility of our approach.

Facial Age Estimation Through the Fusion of Texture and Local Appearance Descriptors

Automatic extraction of soft biometric characteristics from face images is a very prolific field of research. Among these soft biometrics, age estimation can be very useful for several applications, such as advanced video surveillance [5, 12], demographic statistics collection, business intelligence and customer profiling, and search optimization in large databases. However, estimating age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity, and high within-range variance, can be very challenging. These difficulties have been addressed in the past with complex and strongly hand-crafted descriptors, which make it difficult to replicate and compare the validity of posterior classification schemes. This paper presents a simple yet effective approach which fuses and exploits texture- and local appearance-based descriptors to achieve faster and more accurate results. A series of local descriptors and their combinations have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work.

Background subtraction technique based on chromaticity and intensity patterns

This paper presents an efficient real-time method for detecting moving objects in unconstrained environments, using a background subtraction technique. A new background model that combines spatial and temporal information based on similarity measure in angles and intensity between two color vectors is introduced. The comparison is done in RGB color space. A new feature based on chromaticity and intensity pattern is extracted in order to improve the accuracy in the ambiguity region where there is a strong similarity between background and foreground and to cope with cast shadows. The effectiveness of the proposed method is demonstrated in the experimental results and comparison with others approaches is also shown.

Moving Cast Shadows Detection Methods for Video Surveillance Applications

Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).

ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition

Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose the first crowd-sourcing application to collect and label data about apparent age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/~jgarcia/IJCNN2015. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.

Combining where and what in change detection for unsupervised foreground learning in surveillance

Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. HighlightsBuild multi-appearance detectors for unknown and uncontrolled sequences in unsupervised manner.Global discriminative optimization based on latent SVM is able to build accurate multi-class detectors without pretrained detectors.From a noisy initialization (motion cues) learn position, scale and appearance of multiple foreground objects.Combine in an unsupervised way where (motion segmentation) and what (learning procedure) in change detection.Handle an unknown number of objects in an unconstrained scenario.