Abstract: For the background estimation and huge computation problems of background model in the background subtraction blast point detection method, a blast point detection method is proposed based on a hierarchical model of background subtraction via robust dictionary learning. To improve the operation efficiency, a three-tier pyramid hierarchical model is established to divide each frame image into non-overlapping blocks. The blast points are detected from the subtraction between current frame image and image background estimation by using the improved robust dictionary learning method layer by layer. Experimental results on a large number of blast point image sequences show that the proposed method has superior performance in correct detection rate, false positive rate and robustness in comparison with the existing blast point detection method.

Key words: ordnance science and technology, blast point detection, background subtraction, hierarchical model, robust dictionary learning