Ariel Amato

Accurate Moving Cast Shadow Suppression Based on Local Color Constancy Detection

This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions.

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.

Robust real-time background subtraction based on local neighborhood patterns

This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore, both the low computational and space complexities make the presented algorithm feasible for real-time applications.

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.

Divide and conquer: atomizing and parallelizing a task in a mobile crowdsourcing platform

In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.

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).

Solving the multi object occlusion problem in a multiple camera tracking system

An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.

Trajectory occlusion handling with multiple-view distance-minimization clustering

A robust and efficient method for overcoming the negative effects of long-time occlusion in the tracking process is presented. The proposed approach is based on the matching of multiple trajectories in time. Trajectories are sets of 2-D points in time and in a joint ground plane of the world coordinate system. In order to avoid mismatches due to possible measurement outliers, we introduce an integral distance between compared trajectories. The proposed method can also be considered as an interpolation algorithm for a disconnected trajectory during the blackout. Thus this technique solves one of the most difficult problems of occlusion handling: the matching of two unconnected parts of the same trajectory.

Face Detection in Color Images Using Primitive Shape Features

Face detection is a primary step in many applications such as face recognition, video surveillance, human computer interface, and expression recognition. Many existing detection techniques suffer under scale variation, pose variation (frontal vs. profile), illumination changes, and complex backgrounds. In this paper, we present a robust and efficient method for face detection in color images. Skin color segmentation and edge detection are employed to separate all non-face regions from the candidate faces. Primitive shape features are then used to decide which of the candidate regions actually correspond to a face. The advantage of this method is its ability to achieve a high detection rate under varying conditions (pose, scale,…) with low computational cost.

Trajectory fusion for multiple camera tracking

In this paper we present a robust and efficient method to overcome the negative effects of occlusion in the tracking process of multiple agents. The proposed approach is based on the matching of multiple trajectories from multiple views using spatial and temporal information. These trajectories are represented as consecutive points of a joint ground plane in the world coordinate system that belong to the same tracked agent. We introduce an integral distance between compared trajectories, which allows us to avoid mismatches, due to the possible measurement outliers in one frame. The proposed method can also be considered as an interpolation algorithm of a disconnected trajectory during the time of occlusion. This technique solves one of the most difficult problems of occlusion handling, which is a matching of two unconnected parts of the same trajectory.