Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. State-of-the-art methods show good performance in a wide range of situations, but systematically fail when facing more challenging scenarios. Lately, a number of image processing modules inspired in biological models of the human visual system have been explored in different areas of application. This paper proposes a bio-inspired boosting method to address the problem of unsupervised segmentation of moving objects in video that shows the ability to overcome some of the limitations of widely used state-of-the-art methods. An exhaustive set of experiments was conducted and a detailed analysis of the results, using different metrics, revealed that this boosting is more significant when challenging scenarios are faced and state-of-the-art methods tend to fail.

Abstract
The low-complexity encoding, as fundamental requirement of Distributed Video Coding, relies on performing the bulk of computation at decoder, including tasks as the generation of side information and particularly, inter-camera registration in the case of multi-view systems with complete-overlapped views and free motion of the cameras (e.g., video surveillance). In Ciobanu and Corte-Real (Multimedia Tools Appl 48(3):411-436, 2010) we introduced a codec-independent solution for such tasks at decoder. In this paper, we present a multi-view Wyner-Ziv codec (IWZ) designed for the architecture and scenarios from Ciobanu and Corte-Real (2010) (e.g., free motion of the cameras, no a priori knowledge of the instant camera positions, no feedback channel), based on transform domain (DCT), block-based coset coding. We aimed to achieve a compromise between the low encoder complexity and the rate-distortion performance. A detailed evaluation is presented for comparison with conventional coding (Intra 4x4 and Intra 16x16). Practical results show a better overall performance of the proposed codec at low bitrates.

Abstract
A key issue in video object tracking is the representation of the objects and how effectively it discriminates between different objects. Several techniques have been proposed, but without a generally accepted method. While analysis and comparisons of these individual methods have been presented in the literature, their evaluation as part of a global solution has been overlooked. The appearance model for the objects is a component of a video object tracking framework, depending on previous processing stages and affecting those that succeed it. As a result, these interdependencies should be taken into account when analysing the performance of the object description techniques. We propose an integrated analysis of object descriptors and appearance models through their comparison in a common object tracking solution. The goal is to contribute to a better understanding of object description methods and their impact on the tracking process. Our contributions are threefold: propose a novel descriptor evaluation and characterisation paradigm; perform the first integrated analysis of state-of-the-art description methods in a scenario of people tracking; put forward some ideas for appearance models to use in this context. This work provides foundations for future tests and the proposed assessment approach contributes to the informed selection of techniques more adequately for a given tracking application context.

Abstract
Tracking objects in video is a very challenging research topic, particularly when people in groups are tracked, with partial and full occlusions and group dynamics being common difficulties. Hence, its necessary to deal with group tracking, formation and separation, while assuring the overall consistency of the individuals. This paper proposes enhancements to a group management and tracking algorithm that receives information of the persons in the scene, detects the existing groups and keeps track of the persons that belong to it. Since input information for group management algorithms is typically provided by a tracking algorithm and it is affected by noise, mechanisms for handling such noisy input tracking information were also successfully included. Performed experiments demonstrated that the described algorithm outperformed state-of-the-art approaches.

Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. The best solutions are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, BMOG, that significantly boosts the performance of the widely used MOG2 method. The complexity of BMOG is kept low, proving its suitability for real-time applications. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update.

Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task that has attracted the attention of many researchers over the last decades. State-of-the-art methods are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, coined BMOG, that significantly boosts the performance of a widely used method based on a Mixture of Gaussians. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update. The complexity of BMOG is kept low, proving its suitability for real-time applications. BMOG was objectively evaluated using the ChangeDetection.net 2014 benchmark. An exhaustive set of experiments was conducted, and a detailed analysis of the results, using two complementary types of metrics, revealed that BMOG achieves an excellent compromise in performance versus complexity.