Moving object detection for automobiles by the shared use of H.264/AVC motion vectors : innovation report.

Cost is one of the problems for wider adoption of Advanced Driver Assistance Systems (ADAS) in China. The objective of this research project is to develop a low-cost ADAS by the shared use of motion vectors (MVs) from a H.264/AVC video encoder that was originally designed for video recording only. There were few studies on the use of MVs from video encoders on a moving platform for moving object detection. The main contribution of this research is the novel algorithm proposed to address the problems of moving object detection when MVs from a H.264/AVC encoder are used. It is suitable for mass-produced in-vehicle devices as it combines with MV based moving object detection in order to reduce the cost and complexity of the system, and provides the recording function by default without extra cost. The estimated cost of the proposed system is 50% lower than that making use of the optical flow approach. To reduce the area of region of interest and to account for the real-time computation requirement, a new block based region growth algorithm is used for the road region detection. To account for the small amplitude and limited precision of H.264/AVC MVs on relatively slow moving objects, the detection task separates the region of interest into relatively fast and relatively slow speed regions by examining the amplitude of MVs, the position of focus of expansion and the result of road region detection. Relatively slow moving objects are detected and tracked by the use of generic horizontal and vertical contours of rear-view vehicles. This method has addressed the problem of H.264/AVC encoders that possess limited precision and erroneous motion vectors for relatively slow moving objects and regions near the focus of expansion. Relatively fast moving objects are detected by a two-stage approach. It includes a Hypothesis Generation (HG) and a Hypothesis Verification (HV) stage. This approach addresses the problem that the H.264/AVC MVs are generated for coding efficiency rather than for minimising motion error of objects. The HG stage will report a potential moving object based on clustering the planar parallax residuals satisfying the constraints set out in the algorithm. The HV will verify the existence of the moving object based on the temporal consistency of its displacement in successive frames. The test results show that the vehicle detection rate higher than 90% which is on a par to methods proposed by other authors, and the computation cost is low enough to achieve the real-time performance requirement. An invention patent, one international journal paper and two international conference papers have been either published or accepted, showing the originality of the work in this project. One international journal paper is also under preparation

Low relative speed moving vehicle detection using motion vectors and generic line features

This paper presents a new approach to the detection of a vehicle with low relative speed to a monocular moving camera, for complementing moving vehicle detection using motion vectors from H.264/AVC encoder. This method makes use of the generic horizontal line features that exist on most vehicles as a clue of localizing moving vehicles. Further filtering and grouping of these detected lines followed by ego motion compensation can effectively detect moving vehicle with low relative speed for application in advanced driver assistance system. Our test results show a high detection rate of over 90%

Shared-use motion vector algorithm for moving objects detection for automobiles

This paper presents a new motion vector based moving object detection system for use in Advanced Driver Assistance Systems. By the shared-use of motion vectors with a video encoder, the computational cost for motion estimation using optical flow method can be saved, making the system more cost effective. Moving objects are detected by evaluating the planar parallax residuals of macroblocks in the video. The proposed algorithm involves newly proposed “APD” constraints on hypothesis generation and template matching in hypothesis verification. Test results show that moving objects can be detected effectively in scenarios that pose danger to the ego vehicle

A smart moving vehicle detection system using motion vectors and generic line features

This paper presents a novel algorithm for the detection and tracking of low relative speed moving vehicles. The proposed algorithm is particularly suitable for mass-produced in-vehicle devices as it combines with motion vector based moving object detection to form a complete solution for an Advanced Driver Assistance System given its reduction in cost and complexity. The algorithm utilizes motion vectors that are readily available from video encoder output. The region of interest for detection is reduced by ignoring the area above the vanishing line of the captured image, evaluation of the amplitude of motion vectors and identification of the road region. During the evaluation process, a binary image is generated by comparing the gray-level of the captured image to the gray-level of the detected road region. Subsequently, the horizontal and vertical contours of specific areas inside the region of interest are evaluated. Test results show the effectiveness of the algorithm with more than 90 detection rate and the suitability for real-time use with cycle time of less than 66ms

Rationale and design of the screening of pulmonary hypertension in systemic lupus erythematosus (SOPHIE) study

Current guideline-recommended screening for pulmonary hypertension in patients with systemic sclerosis has not been evaluated in systemic lupus erythematosus (SLE), which is disproportionately prevalent in Asians. This multicentre, cross-sectional screening study aims to study the prevalence of pulmonary hypertension among SLE patients using these guidelines, and identify independent predictors and develop a prediction model for pulmonary hypertension in SLE patients. SLE patients from participating centres will undergo an echocardiography- and biomarker-based pulmonary hypertension screening procedure as in the DETECT study. Standard right heart catheterisation will be provided to patients with intermediate or high echocardiographic probability of pulmonary hypertension. Those with low echocardiographic probability will rescreen within 1 year. The primary measure will be the diagnosis and types of pulmonary hypertension and prevalence of pulmonary hypertension in SLE patients. The secondary measures will be the predictors and prediction models for pulmonary hypertension in SLE patients. The estimated sample size is approximately 895 participants. The results of the SOPHIE study will be an important contribution to the literature of SLE-related pulmonary hypertension and may be immediately translatable to real clinical practice. Ultimately, this study will provide the necessary evidence for establishing universal guidelines for screening of pulmonary hypertension in SLE patients