Abstract
This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

Abstract
In this paper we propose a novel fully probabilistic solution to the stereo egomotion estimation problem. We extend the notion of probabilistic correspondence to the stereo case which allow us to compute the whole 6D motion information in a probabilistic way. We compare the developed approach against other known state-of-the-art methods for stereo egomotion estimation, and the obtained results compare favorably both for the linear and angular velocities estimation.

Abstract
Visual motion perception from a moving observer is the most often encountered case in real life situations. It is a complex and challenging problem, although, it can promote the arising of new applications. This article presents an innovative and autonomous robotic system designed for active surveillance and a dense optical flow technique. Several optical flow techniques have been proposed for motion perception however, most of them are too computationally demanding for autonomous mobile systems. The proposed HybridTree method is able to identify the intrinsic nature of the motion by performing two consecutive operations: expectation and sensing. Descriptive properties of the image are retrieved using a tree-based scheme and during the expectation phase. In the sensing operation, the properties of image regions are used by a hybrid and hierarchical optical flow structure to estimate the flow field. The experiments prove that the proposed method extracts reliable visual motion information in a short period of time and is more suitable for applications that do not have specialized computer devices. Therefore, the HybridTree differs from other techniques since it introduces a new perspective for the motion perception computation: high level information about the image sequence is integrated into the estimation of the optical flow. In addition, it meets most of the robotic or surveillance demands and the resulting flow field is less computationally demanding comparatively to other state-of-the-art methods.

Abstract
Over the last few decades, surveillance applications have been an extremely useful tool to prevent dangerous situations and to identify abnormal activities. Although, the majority of surveillance videos are often subjected to different noises that corrupt structured patterns and fine edges. This makes the image processing methods even more difficult, for instance, object detection, motion segmentation, tracking, identification and recognition of humans. This paper proposes a novel filtering technique named robust bilateral and temporal (RBLT), which resorts to a spatial and temporal evolution of sequences to conduct the filtering process while preserving relevant image information. A pixel value is estimated using a robust combination of spatial characteristics of the pixel's neighborhood and its own temporal evolution. Thus, robust statics concepts and temporal correlation between consecutive images are incorporated together which results in a reliable and configurable filter formulation that makes it possible to reconstruct highly dynamic and degraded image sequences. The filtering is evaluated using qualitative judgments and several assessment metrics, for different Gaussian and Salt Pepper noise conditions. Extensive experiments considering videos obtained by stationary and non-stationary cameras prove that the proposed technique achieves a good perceptual quality of filtering sequences corrupted with a strong noise component.

Abstract
This article discusses the motion analysis based on dense optical flow fields and for a new generation of robotic moving systems with real-time constraints. It focuses on a surveillance scenario where an especially designed autonomous mobile robot uses a monocular camera for perceiving motion in the environment. The computational resources and the processing-time are two of the most critical aspects in robotics and therefore, two non-parametric techniques are proposed, namely, the Hybrid Hierarchical Optical Flow Segmentation and the Hybrid Density-Based Optical Flow Segmentation. Both methods are able to extract the moving objects by performing two consecutive operations: refining and collecting. During the refining phase, the flow field is decomposed in a set of clusters and based on descriptive motion properties. These properties are used in the collecting stage by a hierarchical or density-based scheme to merge the set of clusters that represent different motion models. In addition, a model selection method is introduced. This novel method analyzes the flow field and estimates the number of distinct moving objects using a Bayesian formulation. The research evaluates the performance achieved by the methods in a realistic surveillance situation. The experiments conducted proved that the proposed methods extract reliable motion information in real-time and without using specialized computers. Moreover, the resulting segmentation is less computationally demanding compared to other recent methods and therefore, they are suitable for most of the robotic or surveillance applications.

Abstract
This research presents an innovative mobile robotic system designed for active surveillance operations. This mobile robot moves along a rail and is equipped with a monocular camera. Thus, it enhances the surveillance capability when compared to conventional systems (mainly composed by multiple static cameras). In addition, the paper proposes a technique for multi-object tracking called MTMP (Multi-Tracking of Motion Profiles). The MTMP resorts to a formulation based on the Kalman filter and tracks several moving objects using motion profiles. A motion profile is characterized by the dominant flow vector and is computed using the optical flow signature with removal of outliers. A similarity measure based on the Mahalanobis distance is used by the MTMP for associating the moving objects over frames. The experiments conducted in realistic environments have proved that the static perception mode of the proposed robot is able to detect and track multiple moving objects in a short period of time and without using specialized computers. In addition, the MTMP exhibits a good computational performance since it takes less than 5 milliseconds to compute. Therefore, results show that the estimation of motion profiles is suitable for analyzing motion on image sequences.