Search Results

In this paper we present a fast symmetry search and filtering algorithm for monocular vision based pedestrian candidate detection application. First the ROI of symmetry search is focused on the pedestrian leg region, where the background is relatively simple ground plane. Afterward, the search region is divided into 2 × 4 sub blocks and symmetry density and distribution of each sub block is calculated. Finally, by comparing the symmetry density and distribution of the sub blocks, the correct symmetry axis of the pedestrian candidate is search and also some no-pedestrian candidates are filtered out. The results shown in this method are fast, cost effective and well suited for real-time vision applications.

Automotive radar application is a focus in active traffic safety research activities. And accurate lateral position estimation from the leading target vehicle through radar is of great interest. This paper presents a method based on the regression tree, which estimates the rear centroid of leading target vehicle with a long range FLR (Forward Looking Radar) of limited resolution with multiple radar detections distributed on the target vehicle. Hours of radar log data together with reference value of leading vehicle's lateral offset are utilized both as training data and test data as well. A ten-fold cross validation is applied to evaluate the performance of the generated regression trees together with fused decision forest for each percentage of the training data.