Dim-Small Target Detection Based on Adaptive Pipeline Filtering

Abstract

In order to improve the robustness of the pipeline target detection algorithm against strong noises and occlusion, this paper presents an adaptive pipeline filtering algorithm (APFA). In APFA, the velocity and the center of the target are firstly predicted based on the smooth motion trajectory after background suppression. Then, time-domain energy enhancement of targets is adopted to improve the obscure target detection, and adaptively updating the center and radius of the pipeline filter are carried out for targets’ motion variation. Experiments on five different typical scenes show that APFA can improve the robustness of the pipeline filter against strong noises and even when targets are temporarily obscured partially or completely. Simultaneously, APFA can significantly improve the energy and signal-to-noise ratio of targets, and as a result, the target detection rate is significantly promoted on all experiments.

Tiny Vehicle Detection for Mid-to-High Altitude UAV Images Based on Visual Attention and Spatial-Temporal Information

“…The basic idea of pipeline filtering [ 32 , 33 , 34 ] is that in a continuous N-frame image sequence, the position of the candidate object in the previous frame is the center of the pipeline, and the maximum distance that the object can move between frames is the radius of the pipeline. The candidate object detected M times in the pipeline is considered as the real object.…”

“…The candidate object detected M times in the pipeline is considered as the real object. Li et al [ 32 ] improves the traditional pipeline filtering and proposed the adaptive pipeline filtering algorithm. The center and radius of the pipeline filter are updated adaptively according to the motion change of the target, so this algorithm has strong robustness to noise interference and target motion variation.…”

Infrared LSS-Target Detection Via Adaptive TCAIE-LGM Smoothing and Pixel-Based Background Subtraction

“…The detection accuracies of the matched filter [14], the wavelet transform [15], partial differential equations (PDE) [16], and the probabilistic principal component analysis matrix [17] (PCA) are high, but it is almost impossible to achieve real-time detection. Other methods such as particle filtering [18], mobile weighted pipeline filtering [19,20], and likelihood ratio test [21] are based on the multi-frame image, which need to achieve target detection through inter-frame context information. This kind of algorithms has superior accuracy, but aims can't be found effectively if the targets are submerged in the backgrounds or noises [22].…”