A learning algorithm for model-based object detection.

Purpose – Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model-based object detection method which uses only shape-fragment features. Design/methodology/approach – The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi-scales. Findings – The major contributions of this paper are the application of learned shape fragments-based model for object detection in complex environment and a novel two-stage object detection framework. Originality/value – The results presented in this paper are competitive with other state-of-the-art object detection methods. [ABSTRACT FROM AUTHOR]