Target Detection Based on 3D Multi-Component Model and Inverse Projection Transformation

Target detection based on image/video, being involved to deal with the geometry and scale deformation, as well as the change in the form of movement caused by camera imaging, algorithms are always designed complexly. Though, object shelter and adhesion still cannot be well resolved. Considering of that, a new method for target detection on true 3D space based on the inverse projection transformation and a mixing component model is proposed. Firstly, the inverse projective arrays parallel to target local surface are established on 3D space. Then, the 2D image is inversely projected to these planes through 3D point cloud re-projection, and a lot of inverse projective images with target local apparent characteristics are gained. After that, component HOG feature dictionaries are trained using the inverse projective images as samples, and on account of it, sparse decomposition approach is adopted to detect target local components. Finally, 3D centroid clustering for all the components is further used to identify the target. Experiment results indicate that the target detection method on true 3D space based on multi-components model and inverse projection transformation can not only deal with the object occlusion and adhesion perfectly, but also adapt to the multi-angle target detection well, and the accuracy and speed is far beyond that of the algorithm on 2D image.