Application of Plane Homography for Vehicle Speed Calculations in Traffic Accident Investigations

The analysis of vehicle speeds at the moment of traffic accidents is becoming increasingly important. To solve difficult calculations arising from video analysis when the shooting direction of the camera is the same as the driving direction of the vehicles, an improved plane homography method is proposed. This method is based on detecting the frame rate of and extracting all of the key image frames from a surveillance video. Next, a normalizing approach for the control points is proposed to reduce the ill-conditions of matrix calculations when the perspective image is rectified to an orthographic image. The information in the rectified image is then measured and recorded by extracting the mark point information. Finally, the corresponding curve of time and speed for the examined vehicle is created, which can be used to analyze the average braking deceleration of the accident-causing vehicle. This method can directly determine the speed of a vehicle using videos of a traffic accident without any reference to the dimensions of the vehicle itself. Considering an actual traffic accident as an example, the driving trajectories and velocity curves for two vehicles are obtained using this method. The proposed method can effectively overcome the shortcomings of the commonly used speed recognition methods and it is easy to utilize.

Affine Reconstruction Based on Parallel Plane and Infinity Point

Affine reconstruction is to restore the affine shape of the object. Generally, there are two ways of achieving, one is to determine the plane at infinity, another is to determine the plane homography. Using the homography which had determined the plane at infinity achieve affine reconstruction. In this paper, firstly give out the homography of infinity plane and the algorithm of affine reconstruction, then proved: if the scene contains a set of parallel planes and a infinity point, the homography of infinity plane can be obtained and affine reconstruction can be linearly got in the scene. Computer simulation and real experiments show that the linear affine reconstruction algorithm is correct, and the approach has a good precision.

IDENTIFICATION AND MATCHING OF PLANES IN A PAIR OF UNCALIBRATED IMAGES

In this paper, we propose a new method to simultaneously achieve segmentation and dense matching in a pair of stereo images. In contrast to conventional methods that are based on similarity or correlation techniques, this method is based on geometry, and uses correlations only on a limited number of key points. Stemming from the observation that our environment is abundant in planes, this method focuses on segmentation and matching of planes in an observed scene. Neither prior knowledge about the scene nor camera calibration are needed. Using two uncalibrated images as inputs, the method starts with a rough identification of a potential plane, defined by three points only. Based on these three points, a plane homography is then calculated and, used for validation. Starting from a seed region defined by the original three points, the method grows the current region by successive move/confirmation steps until occlusions and/or surface discontinuity occur. In this case, the homography-based mapping of points between the two images will not be valid anymore. This condition is detected by the correlation, used in the confirmation process. In particular, this method grows a region even across different colors as long as the region is planar. Experiments on real images validated our method and showed its capability and performance.