Target Detection In Image Sequences Research Articles

Entropy-Driven Morphological Top-Hat Transformation for Infrared Small Target Detection

Infrared small target detection is a key technique in an infrared system. In the past decade, many methods have concentrated on traditional top-hat transformation, which relies on the hand-crafted shape and value of structural elements. However, these methods are inevitably challenged by the following two aspects: first, the structural elements cannot suppress heavy clutter because the construction of structural elements is always according to the prior information of the target and unable to consider the feature of clutter. Second, adaptively extracting sufficient local feature information for background suppression is hard for the structural element. In this article, we propose an entropy-driven top-hat transformation with guided filter kernel for considering the features of both the clutters and background. First, we propose an entropy-driven top-hat transformation method with our proposed local mean entropy, which can be used to suppress clutter according to the local complex degree of clutter. Then, an adaptive structural element based on a guided filter kernel is further exploited to capture the local feature information of image for background suppression. Finally, an adaptive threshold is combined with our algorithm to achieve target detection in image sequences. The experimental results show that the proposed algorithm is not only robust for suppressing different kinds of backgrounds but can also obtain a higher value of the signal-to-clutter ratio gain and detection accuracy compared with some popular traditional baseline methods and related top-hat methods.

Moving Target Detection Based on Block Projection and Matching

Moving target detection in image sequence for dynamic scene is an important research topic in the field of computer vision. Block projection and matching are utilized for global motion estimation. Then, the background image is compensated applying the estimated motion parameters so as to stabilize the image sequence. Finally, background subtraction is employed in the stabilized image sequence to extract moving targets. Experiment tests denote that the proposed moving target detection algorithm based on block projection and matching can efficiently extract moving targets in dynamic scene.

Improved power-law-detector-based moving small dim target detection in infrared images

A novel algorithm based on an improved power-law detector is proposed to detect moving small dim targets against heavy cluttered background in IR image sequences. As the gray-scale value of a pixel in an image fluctuates when a target passes by, this fluctuation can be viewed as a weak transient signal, and can be detected by the power-law detector, which performs well for transient signal detection. The experimental results illustrate that the proposed algorithm is effective and adaptable for moving small dim target detection in image sequences with heavy clutter

Multiscale appoaches to moving target detection in image sequences

We discuss a novel multiscale approach to the detection of moving objects in a sequence of images. The approach is based in part on a multiframe generalization of an optical flow estimation algorithm previously developed by two of the authors. This algorithm provides an extremely efficient multiscale method for estimating optical flow in an image sequence and allows for the temporal accumulation of motion information. Moving objects in a sequence correspond to discontinuities in the true optical flow and, as a result, the residual image associated with the estimated optical flow can be used as a basis for detecting these objects. We propose an approach to detection based on morphological processing of the residual image, and illustrate its potential on real data.