Abstract
In this paper, by analyzing the characteristics of infrared moving targets, a Symmetric Frame Differencing Target Detection algorithm based on local clustering segmentation is proposed. In consideration of the high real-time performance and accuracy of traditional symmetric differencing, this novel algorithm uses local grayscale clustering to accomplish target detection after carrying out symmetric frame differencing to locate the regions of change. In addition, the mean shift tracking algorithm is also improved to solve the problem of missed targets caused by error convergence. As a result, a kernel-based mean shift target tracking algorithm based on detection updates is also proposed. This tracking algorithm makes use of the interaction between detection and tracking to correct the tracking errors in real time and to realize robust target tracking in complex scenes. In addition, the validity, robustness and stability of the proposed algorithms are all verified by experiments on mid-infrared aerial sequences with vehicles as targets.
Highlights
Detection and tracking of moving targets is a process that involves finding targets of interest in every frame of an image sequence
Combining target tracking with real-time detection in order to realize real-time updating of the target model. Based on these ideas and the characteristics of infrared images, we investigated the use of kernel-based tracking theory [33], which has previously performed well in infrared target tracking [23,26]
After the effectiveness of the SFDLC algorithm had been verified, a tracking experiment based on the target detection results was carried out
Summary
Detection and tracking of moving targets is a process that involves finding targets of interest in every frame of an image sequence. The use of moving imaging platforms such as aircraft gives rise to the problems of background motion and low target resolution [1,2], and correspondingly raises the requirements for the detection and tracking technology that is used. As far as studies to date are concerned, infrared moving target detection algorithms can be roughly divided into background modeling [3,4,5], optical flow [6,7,8] and frame differencing [9,10,11] methods. Bhattacharya et al [14] analyzed and solved the problem of the traditional symmetric
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