Amol Borkar

A Novel Lane Detection System With Efficient Ground Truth Generation

A new night-time lane detection system and its accompanying framework are presented in this paper. The accompanying framework consists of an automated ground truth process and systematic storage of captured videos that will be used for training and testing. The proposed Advanced Lane Detector 2.0 (ALD 2.0) is an improvement over the ALD 1.0 or Layered Approach with integration of pixel remapping, outlier removal, and prediction with tracking. Additionally, a novel procedure to generate the ground truth data for lane marker locations is also proposed. The procedure consists of an original process called time slicing, which provides the user with unique visualization of the captured video and enables quick generation of ground truth information. Finally, the setup and implementation of a database hosting lane detection videos and standardized data sets for testing are also described. The ALD 2.0 is evaluated by means of the user-created annotations accompanying the videos. Finally, the planned improvements and remaining work are addressed.

Robust lane detection and tracking with ransac and Kalman filter

In a previous paper, a simple approach to lane detection using the Hough transform and iterated matched filters was described [1]. This paper extends this work by incorporating an inverse perspective mapping to create a birds-eye view of the road, applying random sample consensus to help eliminate outliers due to noise and artifacts in the road, and a Kalman filter to help smooth the output of the lane tracker.

A layered approach to robust lane detection at night

A layered approach is designed to address many of the real-world problems that an inexpensive lane detection system would encounter. A region of interest is first extracted from the image followed by an enhancement procedure to manipulate the shape of the lane markers. The extracted region is then converted to binary using an adaptive threshold. A model based line detection system hypothesizes lane position. Finally, an iterated matched filtering scheme estimates the final lane position. The developed system shows good performance when tested on real-world data that contains fluctuating illumination and a variety of traffic conditions.

An efficient method to generate ground truth for evaluating lane detection systems

In this document, a new and efficient method to specify the ground truth locations of lane markers is presented. The method comprises of a novel process called Time-Slicing that provided the user with a unique visualization of the video. Coupled with automation via spline interpolation, the quick generation of necessary ground truth information is achieved. Videos recorded from a vehicle while driving on local city roads and highways are marked with ground truth information for use in testing. The performance of a variety of lane detection systems is compared to the ground truth and the error is computed for each system. Finally, quantitative analysis shows that the reference lane detection system presented in [1] produces the most accurate lane detections which is depicted by the smallest error.

Short Wavelength Infrared Face Recognition for Personalization

The paper describes an application of practical technologies to implement a low cost, consumer grade, single chip biometric system based on face recognition using infra-red imaging. The paper presents a system that consists of three stages that contribute in the face detection and recognition process. Each stage is explained with its individual contribution alongside results of tests performed for that stage. The system shows a high recognition rate when full frontal face images are led to the system. The paper further discusses the application based approach in the automotive world with plans for further study. Recognition rates of the overall system are also presented.

Polar randomized hough transform for lane detection using loose constraints of parallel lines

In this paper, we propose a new methodology for detecting lane markers that exploits the parallel nature of lane boundaries on the road. First, the input image is pre-processed and filtered to detect lane marker features. Then, using the Polar Randomized Hough Transform that is introduced in this paper, lines are fitted through the detected features and the orientation of each line is evaluated. By finding near parallel lines separated by a constraint specified distance, false signalling caused by artifacts in the image is greatly reduced. The proposed system was tested using a real world driving videos and showed good results despite the presence of neighboring vehicles, shadows, and irregularities on the road surface.

A template matching and ellipse modeling approach to detecting lane markers

Lane detection is an important element of most driver assistance applications. A new lane detection technique that is able to withstand some of the common issues like illumination changes, surface irregularities, scattered shadows, and presence of neighboring vehicles is presented in this paper. At first, inverse perspective mapping and color space conversion is performed on the input image. Then, the images are cross-correlated with a collection of predefined templates to find candidate lane regions. These regions then undergo connected components analysis, morphological operations, and elliptical projections to approximate positions of the lane markers. The implementation of the Kalman filter enables tracking lane markers on curved roads while RANSAC helps improve estimates by eliminating outliers. Finally, a new method for calculating errors between the detected lane markers and ground truth is presented. The developed system showed good performance when tested with real-world driving videos containing variations in illumination, road surface, and traffic conditions.

Lane Detection and Tracking Using a Layered Approach

A new night-time lane detection system that extends the idea of a Layered Approach [1] is presented in this document. The extension includes the incorporation of (1) Inverse Perspective Mapping (IPM) to generate a bird’s-eye view of the road surface, (2) application of Random Sample Consensus (RANSAC) to rid outliers from the data, and (3) Kalman filtering to smooth the output of the lane tracker. Videos of driving scenarios on local city roads and highways were used to test the new system. Quantitative analysis shows higher accuracy in detecting lane markers in comparison to other approaches.

Detecting lane markers in complex urban environments

In this paper, we present a new methodology for detecting lane markers that is able to withstand many challenging situations like scattered shadows, illumination changes, and presence of neighboring vehicles to name a few. At first, the input image undergoes a perspective removal followed by a color space conversion. Then, the core elements consisting of template matching, lane region merging, and elliptical projections are explained. Finally, the developed system showed good results when tested on 15 minutes of real-world driving videos containing variations in illumination, traffic, and road surface conditions.

A new multi-camera approach for lane departure warning

In this paper, we present a new multi camera approach to Lane Departure Warning (LDW). Upon acquisition, the captured images are transformed to a birds-eye view using a modified perspective removal transformation. Then, camera calibration is used to accurately determine the position of the two cameras relative to a reference point. Lane detection is performed on the front and rear camera images which are combined using data fusion. Finally, the distance between the vehicle and adjacent lane boundaries is determined allowing to perform LDW. The proposed system was tested on real world driving videos and shows good results when compared to ground truth.