Learning Model for Object Detection Based on Local Edge Features
We present a learning model for object detection that uses a novel local edge features. The novel features are motivated by the scheme that use the chamfer distance as a shape comparison measure. The features can be calculated very quickly using a look-up table. Adaboost algorithm is used to select a discriminative edge features set from an over-complete local edge features pool and combine them to form an object detector. To demonstrate our method we trained a system to detect car in complex natural scenes using a single shape model. Experimental results show that our system can extremely rapidly detect objects in varying conditions (translation, scaling, occlusion and illumination) with high detection rate. The results are very competitive with other published object detection schemes. The learning techniques can be extended to detect other objects such as airplanes or pedestrian.
Tang Xusheng Shi Zhelin Li Deqiang Ma Long Chen Dan
Shenyang Institution of Automation,the Chinese Academy of Sciences,Shenyang,110016,China
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
566-570
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）