Traffic congestion wastes fuel and commuters' time, and adds to CO2emissions. Stop-and-go traffic instabilities can be suppresses using bilateral control-which differs from “car following” and adaptive cruise control in that, counter-intuitively, it uses information about thefollowingvehicle (as well as about the leading vehicle). Stability can be proven mathematically, and can be demonstrated in simulation. A physical analog of a sequence of vehicles using bilateral control is a chain of masses connected by springs and dampers-a system which is inherently stable, since it lacks an external energy source. Here, in order to further understand bilateral control and its capacity to suppress instabilities, we move from a microscopic view (interaction of individual vehicles) to a macroscopic view (densities and flow rates). This leads us to the damped wave equation governing traffic under bilateral control. That equation allows us to determine the speed of propagation of disturbances, as well as their rate of decay. The equation is also useful in fine tuning parameters of bilateral control systems.

In the United States, every year, several people whose job takes them to the sides of roads, are injured or killed by roadside collisions. This could be avoided if a warning signal could be sent to them. In this paper, we describe a machine-vision based alerting system which detects and tracks headlamps of cars in night traffic. The system automatically computes a “normal traffic” region in the image. Unusual trajectories of cars are detected when the images of their headlamps move out of that region. The system promptly sends a warning signal once a risk has been identified. The system runs on the Android smart phones, which are mounted on cars or on roadside fixtures.

Traffic flow instabilities have many potential negative consequences, including increased danger of collisions, higher fuel consumption, faster of abrasion of roadways, and reduction of overall traffic throughput. Instabilities occur even if modern forward-looking adaptive cruise control systems are used. In this paper, we present an architecture for controlling vehicle density that uses coordination between adjacent vehicles to attain desired safe following distances, without generating these types of instabilities in the process. The presented architecture has several additional desirable properties, including being robust to sensor failures and measurement inconsistencies, as well as allowing for inhomogeneous vehicle dynamics.

Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel. Building upon this result, we describe two algorithms for estimating spatially invariant blur kernels. In the first method, we directly invert the transform, which is computationally efficient since it is not necessary to also estimate the latent sharp image. This approach is well suited for scenes with a diversity of edges, such as man-made environments. In the second method, we incorporate the Radon transform within the MAP estimation framework to jointly estimate the kernel and the image. While more expensive, this algorithm performs well on a broader variety of scenes, even when fewer edges can be observed. Our experiments show that our algorithms achieve comparable results to the state of the art in general and produce superior outputs on man-made scenes and photos degraded by a small kernel.

We present methods for estimating forces which drive motion observed in density image sequences. Using these forces, we also present methods for predicting velocity and density evolution. To do this, we formulate and apply a Minimum Energy Flow (MEF) method which is capable of estimating both incompressible and compressible flows from time-varying density images. Both the MEF and force-estimation techniques are applied to experimentally obtained density images, spanning spatial scales from micrometers to several kilometers. Using density image sequences describing cell splitting, for example, we show that cell division is driven by gradients in apparent pressure within a cell. Using density image sequences of fish shoals, we also quantify 1) intershoal dynamics such as coalescence of fish groups over tens of kilometers, 2) fish mass flow between different parts of a large shoal, and 3) the stresses acting on large fish shoals.

We investigate a fast pedestrian localization framework that integrates the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features on a data parallel architecture. The salient features of humans are captured by HoG blocks of variable sizes and locations which are chosen by the AdaBoost algorithm from a large set of possible blocks. We use the integral image representation for histogram computation and a rejection cascade in a sliding-windows manner, both of which can be implemented in a data parallel fashion. Utilizing the NVIDIA CUDA framework to realize this method on a Graphics Processing Unit (GPU), we report a speed up by a factor of 13 over our CPU implementation. For a 1280×960 image our parallel technique attains a processing speed of 2.5 to 8 frames per second depending on the image scanning density, which is similar to the recent GPU implementation of the original HoG algorithm in.

We present an integral image algorithm that can run in real-time on a Graphics Processing Unit (GPU). Our system exploits the parallelisms in computation via the NIVIDA CUDA programming model, which is a software platform for solving non-graphics problems in a massively parallel high-performance fashion. This implementation makes use of the work-efficient scan algorithm that is explicated in. Treating the rows and the columns of the target image as independent input arrays for the scan algorithm, our method manages to expose a second level of parallelism in the problem. We compare the performance of the parallel approach running on the GPU with the sequential CPU implementation across a range of image sizes and report a speed up by a factor of 8 for a 4 megapixel input. We further investigate the impact of using packed vector type data on the performance, as well as the effect of double precision arithmetic on the GPU.

Dynamic environment interpretation is of special interest for intelligent vehicle systems. It is expected to provide lane information, target depth, and the image positions of targets within given depth ranges. Typical segmentation algorithms cannot solve the problems satisfactorily, especially under the high-speed requirements of a real-time environment. Furthermore, the variation of image positions and sizes of targets creates difficulties for tracking. In this paper, we propose a sensor-fusion method that can make use of coarse target depth information to segment target locations in video images. Coarse depth ranges can be provided by radar systems or by a vision-based algorithm introduced in the paper. The new segmentation method offers more accuracy and robustness while decreasing the computational load.

Novel algorithms for object recognition are described that directly recover the transformations relating the image to its model. Unlike methods fitting the conventional framework, these new methods do not require exhaustive search for each feature correspondence in order to solve for the transformation. Yet they allow simultaneous object identification and recovery of the transformation. Given hypothesized corresponding regions in the model and data (2D views)-which are from planar surfaces of the 3D objects-these methods allow direct computation of the parameters of the transformation by which the data may be generated from the model. We propose two algorithms: one based on invariants derived from no higher than second order moments of the image, the other via a combination of the affine properties of geometrical and differential attributes of the image. Empirical results on natural images demonstrate the effectiveness of the proposed algorithms. We demonstrate in particular that the differential method is quite stable against perturbations-although not without some error-when compared with conventional methods.

It is shown that shaded images that cannot have originated from a uniformly illuminated, smooth continuous surface with uniform albedo exist. The typical condition where this occurs is when a dark area (corresponding to a region of high gradient) is surrounded by a lighter region (with low gradient). For this to correspond to a real surface, it must be established that there is a local extremum or area of lower gradient inside the dark region. This, in turn, will show up as either a light area in the image or an orientation discontinuity in the surface (thus violating either intensity or smoothness constraints). The impossibility of a shaded image can be established by counting the number of extrema inside a region corresponding to an isolated surface patch.>

In this correspondence, we show how to recover the motion of an observer relative to a planar surface from image brightness derivatives. We do not compute the optical flow as an intermediate step, only the spatial and temporal brightness gradients (at a minimum of eight points). We first present two iterative schemes for solving nine nonlinear equations in terms of the motion and surface parameters that are derived from a least-squares fomulation. An initial pass over the relevant image region is used to accumulate a number of moments of the image brightness derivatives. All of the quantities used in the iteration are efficiently computed from these totals without the need to refer back to the image. We then show that either of two possible solutions can be obtained in closed form. We first solve a linear matrix equation for the elements of a 3 × 3 matrix. The eigenvalue decomposition of the symmetric part of the matrix is then used to compute the motion parameters and the plane orientation. A new compact notation allows us to show easily that there are at most two planar solutions.

In this paper, we derive a closed form solution for recovering the motion of an observer relative to a planar surface directly from image brightness derivatives. We do not compute the optical flow as an intermediate step, only the spatial and temporal intensity gradients at a minimum of 8 points. We solve a linear matrix equation for the elements of a 3×3 matrix. The eigenvalue decomposition of its symmetric part is then used to compute the motion parameters and the plane orientation.

A closed curve in the plane can be described in several ways. We show that a simple representation in terms of radius of curvature versus normal direction has certain advantages. In particular, convolutional filtering of the extended circular image leads to a closed curve. Similar filtering operations applied to some other representations of the curve do not guarantee that the result corresponds to a closed curve. In one case, where a closed curve is produced, it is smaller than the original. A description of a curve can be based on a sequence of smoothed versions of the curve. This is one reason why smoothing of closed curves is of interest.

The curve which has the smallest integral of the square of curvature while passing through two given points with given orientation is searched for.This is the true shape of a spline used in lofting.In computer-aided design, curves have been sought which maximize "smoothness."The curve discussed is the one arising in this way from a commonly used measure of smoothness.The human visual system may use such a curve when it constructs a subjective contour.

Abstract A representation for numbers using two computer words is discussed, where the value represented is the ratio of the corresponding integers. This allows for better dynamic range and relative accuracy than single‐precision fixed point, yet is less costly than floating point arithmetic. The scheme is easy to implement and particularly well suited for minicomputer applications that call for a great deal of numerical compulation. The techniques described have been used to implement a mathematical function subroutine package for a minicomputer as well as a number of applications programs in the machine vision and machine manipulation area.