Foreground Object Detection by Motion-based Grouping of Object Parts

Autor: Shintaro Ono, Zhipeng Wang, Hongbin Zha, Masataka Kagesawa, Jinshi Cui, Katsushi Ikeuchi
Rok vydání: 2014
Předmět:
Zdroj: International Journal of Intelligent Transportation Systems Research. 12:70-82
ISSN: 1868-8659
1348-8503
DOI: 10.1007/s13177-013-0074-8
Popis: Effective video-based detection methods are of great importance to intelligent transportation systems (ITS), and here we propose a method to localize and label objects. The method is able to detect pedestrians and bicycle riders in a complex scene. Our method is inspired by the common fate principle, which is a mechanism of visual perception in human beings, and which states tokens moving or functioning in a similar manner tend to be perceived as one unit. Our method embeds the principle in an Implicit Shape Model (ISM). In our method, keypoint-based object parts are firstly detected and then grouped by their motion patterns. Based on the grouping results, when the object parts vote for object centers and labels, each vote belonging to the same object part is assigned a weight according to its consistency with the votes of other object parts in the same motion group. Afterwards, the peaks, which correspond to detection hypotheses on the Hough image formed by summing up all weighted votes, become easier to find. Thus our method performs better in both position and label estimations. Experiments show the effectiveness of our method in terms of detection accuracy.
Databáze: OpenAIRE
Externí odkaz: