Abstract-In this paper, we present a method for drivable road detection by extracting its specular intrinsic feature from an image. The resulting detection is then used in a stereo vision-based 3D road parameters extraction algorithm. A substantial representation of the road surface, called axis-calibration, is represented as an angle in logchromaticity space. This feature provides an invariance to road surface under illuminant conditions with shadow or not. We also add a sky removal function in order to eliminate the negative effects of sky light on axiscalibration result. Then, a confidence interval calculation helps the pixels' classification to speed up the detection processing. At last, the approach is combined with a stereovision based method to filter out false detected pixels and to obtain precise 3D road parameters. The experimental results show that the proposed approach can be adapted for real-time ADAS system in various driving conditions.

Vision systems provide a large functional spectrum for perception applications and, in recent years, they have demonstrated to be essential in the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles. In this context, this paper presents an on-road objects detection approach improved by our previous work in defining the traffic area and new strategy in obstacle extraction from U-disparity.Then, a modified particle filtering is proposed for multiple object tracking. The perception strategy of the proposed vision-only detection system is structured as follows : First, a method based on illuminant invariant image is employed at an early stage for free road space detection. A convex hull is then constructed to generate a region of interest (ROI) which includes the main traffic road area. Based on this ROI, an U-disparity map is built to characterize on-road obstacles. In this approach, connected regions extraction is applied for obstacles detection instead of standard Hough Transform. Finally, a modified particle filter framework is employed for multiple targets tracking based on the former detection results. Besides, multiple cues, such as obstacle's size verification and combination of redundant detections, are embedded in the system to improve its accuracy. Our experimental findings demonstrates that the system is effective and reliable when applied on different traffic video sequences from a public database.

Vision-based dynamic objects motion segmentation can significantly help to understand the context around vehicles, and furthermore improve road traffic safety and autonomous navigation. Therefore, moving object detection in complex traffic scene becomes an inevitable issue for ADAS and autonomous vehicles. In this paper, we propose an approach that combines different multiple views geometry constraints to achieve moving objects detection using only a monocular camera. Self-assigned weights are estimated online moderating the contribution of each constraint. Such a combination enhances the detection performance in degenerated situations. According to the experimental results, the proposed approach provides accurate moving objects detections in dynamic traffic scenarios with large camera motions.

In this paper, we propose an effective approach for moving object detection based on modeling the ego-motion uncertainty and using a graph-cut based motion segmentation. First, the relative camera pose is estimated by minimizing the sum of reprojection errors and its covariance matrix is calculated using a first-order errors propagation method. Next, a motion likelihood for each pixel is obtained by propagating the uncertainty of the ego-motion to the Residual Image Motion Flow (RIMF). Finally, the motion likelihood and the depth gradient are used in a graph-cut based approach as region and boundary terms respectively, in order to obtain the moving objects segmentation. Experimental results on real-world data show that our approach can detect dynamic objects which move on the epipolar plane or that are partially occluded in complex urban traffic scenes.

The allocation of innovation-related elements is influenced by intangible elements such as technological information, which affects knowledge capital, human capital, and material element investment, resulting in the stochastic change of technological innovation efficiency. The endogenous change in technological information in element investment reduces friction, lowers costs, improves efficiency, and gradually reduces the deviation between knowledge capital investment and technological innovation efficiency. In terms of small knowledge capital investment characterized by the low demand for technological information and a single source, it is easier to make more-accurate predictions, so the stochastic change in technological innovation efficiency tends to be gentle. The endogenous change in technological information continuously increases the proportion of replacing other elements with knowledge capital investment, and the efficiency of technological innovation improves steadily. Under the condition of unchanged investment in other elements, there is a great difference in the duration of technological information between the simultaneous selection and the successive selection of knowledge capital investment. When one-time instantaneous information and continuous complete information are respectively acquired, the stochastic variation of technological innovation efficiency is obvious. On the basis of the technological innovation data of 287 listed companies in eight industries, this paper compares and analyzes the measurement results of GMM and OLS to verify the above findings.