A low cost, real time vehicle lane position detection system is provided for determining and maintaining the position of the vehicle on a highway. The system comprises an image sensor mounted on the front of an automotive vehicle and an integrated processor for performing real-time lane mark detection. The sensor/processor system identifies highway lane marks on the detector image plane by using a nonlinear resistive network for detecting outliers. A line detection algorithm, such as the Hough transform, is used to determine the lane marks from the outliers on the image plane. Because the expected lane position can be determined in advance, an added degree of signal-to-noise discrimination is achieved by providing feedback to the processor for outlier detection. The position of the vehicle in the lane is determined and tracked from the position of the detected lane marks on the image plane given the sensor position and optical geometry.

Vehicle lane position detection system

A method for determining the speed of a vehicle using a camera. The method automatically compensates an apparent speed determination for inaccuracies due to the position of the camera with respect to the vehicle. The invention also includes a method for calibrating a camera to compensate for inaccuracies due to the position of the camera.

Vehicle speed monitoring system

A color information creating part calculates the average color of captured image data in units of macro blocks, and determines to which color index the average color of each macro block corresponds. Color arrangement information formed by associating color information with the macro blocks is stored in a color index memory. A histogram creating part creates color histograms representative of the distribution of macro blocks added with color information corresponding to aimed color indexes. Based on the color arrangement information and the color histograms, a color application execution part recognizes objects, surroundings, or the situation of a driver's own vehicle without performing area splitting (shape acquisition) of an image.

Environment recognition device

A system and method for detecting and filtering non-violation events in a traffic light violation prediction and recording system, including at least one violation prediction image capturing device, such as a video camera, and a violation prediction unit. The prediction unit generates a prediction reflecting a probability that the vehicle will violate a red light phase of the traffic signal. A non-violation event filter determines whether the vehicle approaching the traffic signal is actually performing a non-violation action. Non-violation events may include a variety of actions performed by the vehicle, and are fully configurable to meet the needs and policies of various specific intersections and jurisdictions. When the non-violation event filter determines that the vehicle is performing a non-violation action, it causes some number of resources that may have been allocated to recording the non-violating vehicle, and/or prevents further resources from being allocated to such recording.

Traffic violation detection at an intersection employing a virtual violation line

An accident avoidance system in which a substantially continuous data stream consisting of multiple vehicle velocity and location information is processed by means that will generate advisories to vehicles that are approaching potentially hazardous situations. These advisories are in advance of the vehicles hazard encounter so that driver corrective action can be taken to avoid collision. For each vehicle along a monitored length of highway, means are provided for computing the vehicles actual acceleration or deceleration and for computing the deceleration required for the vehicle to avoid an accident. Collision avoidance advisories are generated whenever the vehicle's acceleration or deceleration deviates from the deceleration required to avoid a collision. Means are provided for detecting vehicles that may be operated by drivers whose performance is impaired.

Highway traffic accident avoidance system

A vehicular traffic monitoring system incorporates an array of photosensors and a nonlinear resistive network for identifying, locating, and processing outliers in sensor images of a highway or intersection. The camera system can be mounted on a pole or overpass to provide an image of the roadway or intersection. Areas of the outlier network ("video loops") are designated to correspond to selected areas of the roadway. Images are received by the outlier detection network with all data path switches closed between sensor elements and their corresponding network nodes. The system detects the presence of objects in the image by comparing the brightness or intensity of each pixel with that of the background. If the intensity of a pixel is significantly different from the background level, the data path switch corresponding to that pixel is opened. A readout of the state of all the switches in the network yields a map of outlier points for each video frame. The outlier map is connected to a data processing system to identify and locate outlier points in the image. The detection of a threshold number of outliers in a video loop indicates the presence of a vehicle at the corresponding area of the roadway. The processor, having a greatly reduced computational load without extensive image processing, simply measures and transmits traffic data such as the number and speed of vehicles passing through the video loops.

Vehicular traffic monitoring system

The authors describe an algorithm for discarding outliers in noisy and possibly sparse sensor data. The authors demonstrate a network that incorporates robustness in its computation and the network settles to its final solution in a few time constants. The work is a modification of a resistive network which provides for detection and removal of outliers in image segmentation. A nonlinear resistive network is used to isolate an input point from the rest of the network when the input point differs significantly from the neighborhood average. The resistive network outlier algorithm has a simple elegant embodiment in analog real-time VLSI hardware. The authors demonstrate the algorithm with simulations on a laser radar image. >

http://ieeexplore.ieee.org/iel2/587/4026/00155230.pdf

Discarding outliers using a nonlinear resistive network

A nonlinear resistive network, which includes switches in the data paths, is provided for identifying, isolating, and/or rejecting outliers in a sensor image. During normal operation, images are received with all the switches in a closed (conducting) state. If the data at a given pixel is different from its immediate neighbors by a predetermined threshold voltage, its switch is opened. A readout of the state of all the switches in the network yields a map of points sources, and a readout of the network voltages yields a noise-free image. Because the threshold voltage can be controlled externally, various strategies may be implemented for identification of the outliers in a computer vision system.

Nonlinear resistive network for isolating outliers in sensor images

We describe two successfully working, analog VLSI vision circuits that move beyond pixel-based early vision algorithms. One circuit, implementing the dynamic wires model, provides for dedicated lines of communication among groups of pixels that share a common property. The chip uses the dynamic wires model to compute the arclength of visual contours. Another circuit labels all points inside a given contour with one voltage and all other with another voltage. Its behavior is very robust, since small breaks in contours are automatically sealed, providing for Figure-Ground segregation in a noisy environment. Both chips are implemented using networks of resistors and switches and represent a step towards object level processing since a single voltage value encodes the property of an ensemble of pixels.

/pdf/object-based-analog-vlsi-vision-circuits-2rvi37gxmr.pdf

Object-Based Analog VLSI Vision Circuits

Describes two analog VLSI chips that perform object-based computations. The first chip computes length of various contours in an image and the second chip performs figure-ground separation of a scene. >

/pdf/object-based-analog-vlsi-vision-circuits-14oros6gve.pdf

Shih-Chii Liu

Papers

Vehicular traffic monitoring system

Discarding outliers using a nonlinear resistive network

Nonlinear resistive network for isolating outliers in sensor images

Object-Based Analog VLSI Vision Circuits

Object-based analog VLSI vision circuits