Fast Object Detection Using Edge Fragment-Based Features

We present a learning model for object detection in images with complex background.Novel local edge feature with chamfer distance as shape comparison measure are used to form a dictionary of templates.The features can be calculated very quickly using the Integral Image technique.Bagging-Adaboost algorithm is applied to select a discriminative edge features set and combine them to form an object detector.Floating search post optimization procedure is included to remove base classifiers causing higher error rates.The resulting classifier consists of fewer base classifiers yet achieves better generalization performance.Experimental results on UCUI image test sets show that our system can extremely quickly detect objects in varying conditions(translation,scaling,occlusion and illumination) with high detection rates.The results are very competitive with some other published object detection schemes.The speed of detection is much faster than that of existing schemes.