In this paper, we analyse trends and challenges that the automotive domain is living the last years. We then focus on the agile transformation and the need to compliance with safety and security standards. Finally, we discuss on the significance of continuous compliance.

Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain and fail to capture the information present in 3D space. In this work we propose a principled way to generate object labelling in 3D. Our method builds a triangulated meshed representation of the scene from multiple depth estimates. We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene. In this framework, we are able to generate object hypotheses by combining information from multiple sources: geometric properties (from the 3D mesh), and appearance properties (from images). We demonstrate the robustness of our framework in both indoor and outdoor scenes. For indoor scenes we created an augmented version of the NYU indoor scene dataset (RGBD images) with object labelled meshes for training and evaluation. For outdoor scenes, we created ground truth object labellings for the KITTY odometry dataset (stereo image sequence). We observe a significant speed-up in the inference stage by performing labelling on the mesh, and additionally achieve higher accuracies.

In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset.

In order to create a successful software product and assure its quality, it is not enough to fulfill the functional requirements, it is also crucial to find the right balance among competing quality requirements (QR). An extended, previously piloted, interview study was performed to identify specific challenges associated with the selection, tradeoff, and management of QR in industrial practice. Data were collected through semistructured interviews with 11 product managers and 11 project leaders from 11 software companies. The contribution of this study is fourfold: First, it compares how QR are handled in two cases, companies working in business-to-business markets and companies that are working in business-to-consumer markets. These two are also compared in terms of impact on the handling of QR. Second, it compares the perceptions and priorities of QR by product and project management, respectively. Third, it includes an examination of the interdependencies among quality requirements perceived as most important by the practitioners. Fourth, it characterizes the selection and management of QR in downstream development activities.

Robustness of a software system is defined as the degree to which the system can behave ordinarily and in conformance with the requirements in extraordinary situations. By increasing the robustness many failures which decrease the quality of the system can be avoided or masked. When it comes to specifying, testing and assessing software robustness in an efficient manner the methods and techniques are not mature yet. This paper presents RobusTest, a framework for testing robustness properties of a system with currently focus on timing issues. The expected robust behavior of the system is formulated as properties. The properties are then used to automatically generate robustness test cases and assess the results. An implementation of RobusTest in Java is presented here together with results from testing different, open-source implementations of the XMPP instant messaging protocol. By executing 400 test cases that were automatically generated from properties on two such implementations we found 11 critical failures and 15 nonconformance problems as compared to the XMPP specification.

Requirements prioritization is recognized as an important but challenging activity in software product development. For a product to be successful, it is crucial to find the right balance among competing quality requirements. Although literature offers many methods for requirements prioritization, the research on prioritization of quality requirements is limited. This study identifies how quality requirements are prioritized in practice at 11 successful companies developing software intensive systems. We found that ad-hoc prioritization and priority grouping of requirements are the dominant methods for prioritizing quality requirements. The results also show that it is common to use customer input as criteria for prioritization but absence of any criteria was also common. The results suggests that quality requirements by default have a lower priority than functional requirements, and that they only get attention in the prioritizing process if decision-makers are dedicated to invest specific time and resources on QR prioritization. The results of this study may help future research on quality requirements to focus investigations on industry-relevant issues.

The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene are modeled employing fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the structure of the scene and characterises the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners which operate on image intensity discriminative features which are defined on small patches and fast to compute. A database which is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator which requires a ribbon like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2-D delineation and fast 3-D tracking and compare its performance with other existing methods for line search boundary detection.

In this paper we show how online images can be automatically exploited for scene visualization and reconstruction starting from a mere visual query provided by the user. A visual query is used to retrieve images of a landmark place using a visual search engine. These images are used to reconstruct robust 3–D features and camera poses in projective space. Novel views are then rendered corresponding to a virtual camera flying smoothly through the projective space by triangulation of the projected points in the output view. We introduce a method to fuse the rendered novel views from all input images at each virtual view point by computing their intrinsic image and illuminations. This approach allows us to remove the occlusions and maintain consistent and controlled illumination throughout the rendered sequence. We demonstrate the performance of our prototype system on two landmark structures. 1

The ForceFeedback Slider (FFS) is a one-dimensional actuated slider using a motor to produce tangible interaction with position and force as input and output parameters. To create a new concept, we have built a mixing desk, placed six FFSs (two implemented here) into a partially realized SliderBox, and added a LED and two toggle buttons to each slider for additional interactivity. We have developed a tool called BounceSlider for improvising music. This application for real time music performance and composition uses a slider handle that can act as a ball. Users can lift and release the handle to set the ball in motion and produce a particular sound each time it bounces against the baseline. Based on physical characteristics, the user can create different sounds and loops by changing two settings: gravity (speed) and bounce type (ball physical characteristics). BounceSlider allows the user to create and save loops of Musical Instrument Digital Interface (Midi) with up to five sounds at a time.

This paper proposes a machine learning approach to the problem of modelbased contour tracking for rigid or deformable objects. The motion of the target is calculated by tracking its contours in a video sequence. We develop a probabilistic representation of contours that allows robust contour tracking in presence of texture and clutter. We use boosting to train a predictor of the conditional probability of texture transition, given the pixel intensities. The most likely connected contours are obtained by maximizing the posterior probability of object model parameters. The probabilistic formulation allows spatial connectivity of the contours to be formulated in a natural manner. For deformable objects, we use a Hidden Markov Model to calculate the joint law of the conditional probabilities of contour points while for rigid objects geometric properties of the model are used in a framework of random sample consensus algorithm to find the optimal model pose. We demonstrate that the proposed method is fast and robust for tracking deformable and rigid objects. We also compare our algorithm to several other contour tracking methods. 1

We propose a fast texture-segmentation approach to the problem of 2D and 3D model-based contour tracking, which is suitable for real-time or interactive applications. Our approach relies on detecting texture boundaries in the direction normal to the contour boundaries and on using a hidden Markov model to link these boundary points in the other direction. The probabilities that appear in this computation closely relate to texture entropy and Kullback-Leibler divergence, a property we use to compute and update dynamic texture models. We demonstrate results both in the context of interactive 2D delineation and fast 3D tracking

We propose a novel method to detect human body contours in presence of clutter and complex texture. Contours are extracted using a novel Markovbased approach which learns a texture along a given scanline in order to detect texture crossings. In contrast to conventional silhouette detection algorithms based on gradient, our texture boundary detection method allows extraction of silhouettes of textured and non-textured objects under difficult conditions such as having a cluttered/moving background. We demonstrate on demanding examples of monocular body tracking that our proposed method yields better results than gradient-based techniques. 1.

We present a method for performing data association that handles complex motion models while increasing the robustness of tracking and being suitable for real-time applications. Instead of using motion model in standard recursive fashion, we robustly fit it over multiple frames simultaneously. This allows us to naturally handle arbitrarily complex motion models, to automate the initialization and to deal with occlusion and false alarms. This is effective even if the motion model is not entirely accurate and if there are frequent false-negatives and false-positives. Our algorithm is easy to implement and we show its performances on two real examples of complex motion tracking.

In augmented reality applications, tracking and registration of both cameras and objects is required because, to combine real and rendered scenes, we must project synthetic models at the right location in real images. Although much work has been done to track objects of interest, initialization of theses trackers often remains manual. Our work aims at automating this step by integrating object recognition and tracking into an AR system. Our emphasis is on the initialization phase of the tracking. We address all the three major aspects of the problem of model-to-image registration: feature detection, correspondence and pose estimation. We have developed a novel approach based on facet detection that greatly reduces the number of possible feature correspondences making it possible to directly compute the transformation which best maps 3-D object to the image plane. We will argue that this approach offers a one-fold speed-up over existing methods. Results of our AR system which integrates initialization and tracking are shown. Our method takes about 5 seconds on our example images.