Monocular tracking of the human arm in 3D: real-time implementation and experiments

We have developed a system capable of tracking a human arm in 3D and in real time. The system is based on a previously developed algorithm for 3D tracking which requires only a monocular view and no special markers on the body. In this paper we describe our real-time system and the insights gained from real-time experimentation

Recursive Motion and Structure Estimation with Complete Error Characterization

We present an algorithm that perfom recursive estimation of ego-motion andambient structure from a stream of monocular Perspective images of a number of feature points. The algorithm is based on an Extended Kalman Filter (EKF) that integrates over time the instantaneous motion and structure measurements computed by a 2-perspective-views step. Key features of our filter are (I) global observability of the model, (2) complete on-line characterization of the uncertainty of the measurements provided by the two-views step. The filter is thus guaranteed to be well-behaved regardless of the particular motion undergone by the observel: Regions of motion space that do not allow recovery of structure (e.g. pure rotation) may be crossed while maintaining good estimates of structure and motion; whenever reliable measurements are available they are exploited. The algorithm works well for arbitrary motions with minimal smoothness assumptions and no ad hoc tuning. Simulations are presented that illustrate these characteristics

Observability/Identifiability of Rigid Motion under Perspective Projection

The "visual motion" problem consists of estimating the motion of an object viewed under projection. In this paper we address the feasibility of such a problem. We will show that the model which defines the visual motion problem for feature points in the euclidean 3D space lacks of both linear and local (weak) observability. The locally observable manifold is covered with three levels of lie differentiations. Indeed, by imposing metric constraints on the state-space, it is possible to reduce the set of indistinguishable states. We will then analyze a model for visual motion estimation in terms of identification of an Exterior Differential System, with the parameters living on a topological manifold, called the "essential manifold", which includes explicitly in its definition the forementioned metric constraints. We will show that rigid motion is globally observable/identifiable under perspective projection with zero level of lie differentiation under some general position conditions. Such conditions hold when the viewer does not move on a quadric surface containing all the visible points

Dynamic Estimation of Rigid Motion from Perspective Views via Recursive Identification of Exterior Differential Systems with Parameters on a Topological Manifold

We formulate the problem of estimating the motion of a rigid object viewed under perspective projection as the identification of a dynamic model in Exterior Differential form with parameters on a topological manifold. We first describe a general method for recursive identification of nonlinear implicit systems using prediction error criteria. The parameters are allowed to move slowly on some topological (not necessarily smooth) manifold. The basic recursion is solved in two different ways: one is based on a simple extension of the traditional Kalman Filter to nonlinear and implicit measurement constraints, the other may be regarded as a generalized "Gauss-Newton" iteration, akin to traditional Recursive Prediction Error Method techniques in linear identification. A derivation of the "Implicit Extended Kalman Filter" (IEKF) is reported in the appendix. The ID framework is then applied to solving the visual motion problem: it indeed is possible to characterize it in terms of identification of an Exterior Differential System with parameters living on a C0 topological manifold, called the "essential manifold". We consider two alternative estimation paradigms. The first is in the local coordinates of the essential manifold: we estimate the state of a nonlinear implicit model on a linear space. The second is obtained by a linear update on the (linear) embedding space followed by a projection onto the essential manifold. These schemes proved successful in performing the motion estimation task, as we show in experiments on real and noisy synthetic image sequences

Reliability prediction for the vehicles equipped with advanced driver assistance systems (ADAS) and passive safety systems (PSS)

The human error has been reported as a major root cause in road accidents in today’s world. The human as a driver in road vehicles composed of human, mechanical and electrical components is constantly exposed to changing surroundings (e.g., road conditions, environment)which deteriorate the driver’s capacities leading to a potential accident. The auto industries and transportation authorities have realized that similar to other complex and safety sensitive transportation systems, the road vehicles need to rely on both advanced technologies (i.e., Advanced Driver Assistance Systems (ADAS)) and Passive Safety Systems (PSS) (e.g.,, seatbelts, airbags) in order to mitigate the risk of accidents and casualties. In this study, the advantages and disadvantages of ADAS as active safety systems as well as passive safety systems in road vehicles have been discussed. Also, this study proposes models that analyze the interactions between human as a driver and ADAS Warning and Crash Avoidance Systems and PSS in the design of vehicles. Thereafter, the mathematical models have been developed to make reliability prediction at any given time on the road transportation for vehicles equipped with ADAS and PSS. Finally, the implications of this study in the improvement of vehicle designs and prevention of casualties are discussed

An Active Visual Estimator for Dexterous Manipulation

We present a working implementation of a dynamics based architecture for visual sensing. This architecture provides field rate estimates of the positions and velocities of two independent falling balls in the face of repeated visual occlusions and departures from the field of view. The practical success of this system can be attributed to the interconnection of two strongly nonlinear dynamical systems: a novel triangulating state estimator; and an image plane window controller. We detail the architecture of this active sensor, provide data documenting its performance, and offer an analysis of its soundness in the form of a convergence proof for the estimator and a boundedness proof for the manager