Detection of point targets amid cluttered background in IR imagery for IRST and MAWS applications

In this paper, we propose an evolutionary correlation filtering approach for solving pose estimation in noncontinuous video sequences. The proposed algorithm computes the linear correlation between the input scene containing a target in an unknown environment and a bank of matched filters constructed from multiple views of the target and estimates of statistical parameters of the scene. An evolutionary approach for finding the optimal filter that produces the highest matching score in the correlator is implemented. The parameters of the filter bank evolve through generations to refine the quality of pose estimation. The obtained results demonstrate the robustness of the proposed algorithm in challenging image conditions such as noise, cluttered background, abrupt pose changes, and motion blur. The performance of the proposed algorithm yields high accuracy in terms of objective metrics for pose estimation in noncontinuous video sequences.

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Min-local-LoG filter for detecting small targets in cluttered background

Electronics Letters ◽

10.1049/el.2010.2066 ◽

2011 ◽

Vol 47 (2) ◽

pp. 105 ◽

Cited By ~ 20

Author(s):

S. Kim

Keyword(s):

Cluttered Background ◽

Small Targets

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Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1037.373 ◽

2014 ◽

Vol 1037 ◽

pp. 373-377 ◽

Cited By ~ 1

Author(s):

Teng Fei ◽

Liu Qing ◽

Lin Zhu ◽

Jing Li

Keyword(s):

Kalman Filter ◽

Visual Tracking ◽

State Of The Art ◽

Experimental Results ◽

Closed Circuit ◽

Video Sequences ◽

Inland Waterway ◽

Cluttered Background ◽

Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

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