Dynamic Point Clustering with Line Constraints for Moving Object Detection in DAS

• Published in: IEEE signal processing letters Vol. 21; no. 10; pp. 1255 - 1259
• Main Authors: Park, Jonghee, Yoon, Ju Hong, Park, Min-Gyu, Yoon, Kuk-Jin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; Clustering methods; Cost function; Criteria; Driver assistance system; dynamic object detection; Dynamical systems; Dynamics; Geometric constraints; Image detection; line feature; Mathematical analysis; Motion segmentation; point clustering; Segments; Three-dimensional displays; Vehicle dynamics; Vehicles
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2014.2330058

In this letter, we propose a robust dynamic point clustering method for detecting moving objects in stereo image sequences, which is essential for collision detection in driver assistance system. If multiple objects with similar motions are located in close proximity, dynamic points from different moving objects may be clustered together when using the position and velocity as clustering criteria. To solve this problem, we apply a geometric constraint between dynamic points using line segments. Based on this constraint, we propose a variable K-nearest neighbor clustering method and three cost functions that are defined between line segments and points. The proposed method is verified experimentally in terms of its accuracy, and comparisons are also made with conventional methods that only utilize the positions and velocities of dynamic points.