How much of driving is pre-attentive?

Driving a car in an urban setting is an extremely difficult problem, incorporating a large number of complex visual tasks; however, this problem is solved daily by most adults with little apparent effort. This paper proposes a novel vision-based approach to autonomous driving that can predict and even anticipate a driver's behavior in real time, using preattentive vision only. Experiments on three large datasets totaling over 200 000 frames show that our preattentive model can (1) detect a wide range of driving-critical context such as crossroads, city center, and road type; however, more surprisingly, it can (2) detect the driver's actions (over 80% of braking and turning actions) and (3) estimate the driver's steering angle accurately. Additionally, our model is consistent with human data: First, the best steering prediction is obtained for a perception to action delay consistent with psychological experiments. Importantly, this prediction can be made before the driver's action. Second, the regions of the visual field used by the computational model strongly correlate with the driver's gaze locations, significantly outperforming many saliency measures and comparable to state-of-the-art approaches.European Commission’s Seventh Framework Programme (FP7/2007-2013

Visual Attention Mechanism for a Social Robot

This paper describes a visual perception system for a social robot. The central part of this system is an artificial attention mechanism that discriminates the most relevant information from all the visual information perceived by the robot. It is composed by three stages. At the preattentive stage, the concept of saliency is implemented based on ‘proto-objects’ [37]. From these objects, different saliency maps are generated. Then, the semiattentive stage identifies and tracks significant items according to the tasks to accomplish. This tracking process allows to implement the ‘inhibition of return’. Finally, the attentive stage fixes the field of attention to the most relevant object depending on the behaviours to carry out. Three behaviours have been implemented and tested which allow the robot to detect visual landmarks in an initially unknown environment, and to recognize and capture the upper-body motion of people interested in interact with it

Aspects of an open architecture robot controller and its integration with a stereo vision sensor.

The work presented in this thesis attempts to improve the performance of industrial robot systems in a flexible manufacturing environment by addressing a number of issues related to external sensory feedback and sensor integration, robot kinematic positioning accuracy, and robot dynamic control performance. To provide a powerful control algorithm environment and the support for external sensor integration, a transputer based open architecture robot controller is developed. It features high computational power, user accessibility at various robot control levels and external sensor integration capability. Additionally, an on-line trajectory adaptation scheme is devised and implemented in the open architecture robot controller, enabling a real-time trajectory alteration of robot motion to be achieved in response to external sensory feedback. An in depth discussion is presented on integrating a stereo vision sensor with the robot controller to perform external sensor guided robot operations. Key issues for such a vision based robot system are precise synchronisation between the vision system and the robot controller, and correct target position prediction to counteract the inherent time delay in image processing. These were successfully addressed in a demonstrator system based on a Puma robot. Efforts have also been made to improve the Puma robot kinematic and dynamic performance. A simple, effective, on-line algorithm is developed for solving the inverse kinematics problem of a calibrated industrial robot to improve robot positioning accuracy. On the dynamic control aspect, a robust adaptive robot tracking control algorithm is derived that has an improved performance compared to a conventional PID controller as well as exhibiting relatively modest computational complexity. Experiments have been carried out to validate the open architecture robot controller and demonstrate the performance of the inverse kinematics algorithm, the adaptive servo control algorithm, and the on-line trajectory generation. By integrating the open architecture robot controller with a stereo vision sensor system, robot visual guidance has been achieved with experimental results showing that the integrated system is capable of detecting, tracking and intercepting random objects moving in 3D trajectory at a velocity up to 40mm/s

Image flow: photography on tap

This essay is about the phenomenon of mass, mobile photographic images in a digital, networked context. In response to recent writings that challenge the relevance of the close reading of singular images, it proposes rethinking the opposition between singular images and images en masse through philosophical ideas of multiplicity and, in particular, via the concept of image flow. It examines four connected contexts in which concepts of flow have been used: in discourses surrounding the internet and digital media, where it is used to naturalise these media; in psychology, where ideas of flow underpin descriptions of consciousness and human/ animal perception; in robotics and Artificial Intelligence, where ideas of flow from psychology joined with a move away from dependence on representation to facilitate increasingly autonomous mobile machines; and finally, in studies of television, where the on-tap transmission of images has been understood in terms of a flow that articulates or choreographs bodies and attention, connecting the rhythms and temporality of private and public space, cities and suburbs. This model of flow, in particular, allows for analysis that operates across different scales, and undoes oppositions of scale and surface / depth that pervade recent photography theory

Learning and Using Context on a Humanoid Robot Using Latent Dirichlet Allocation

2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob), Genoa, Italy, 13-16 October 2014In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts. We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.Scientific and Technological Research Council of Turkey (TUBiTAK

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research

Tied factor analysis for face recognition across large pose differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches

Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research