Moving target detection by nonlinear adaptive filtering on temporal profiles in infrared image sequences

Liu, Delian; Li, Zhaohui; Wang, Xiaorui; Zhang, Jianqi

doi:10.1016/j.infrared.2015.09.003

Cited by 38 publications

(15 citation statements)

References 27 publications

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“…To evaluate the effectiveness and robustness of our spatialtemporal filter TVPCF, 5 multi-frame small target detection methods [28], [38]- [40], [42] are compared on 4 real sequences. All parameters of these methods are set according to their references.…”

Section: Multi-frame Target Enhancement and Detection Resultsmentioning

confidence: 99%

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Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Gao

Lin

2019

IEEE Access

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Infrared small target detection is challenging due to the various background and low signalto-clutter ratios. Considering the information deficiency faced by single spatial or temporal information, we construct a low false alarm spatial and temporal filter for infrared small target detection. A multiscale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level. Then, a temporal variance filter is used to remove small broken cloud regions and suppress noise at a fine level. By integrating these two methods, infrared small targets can be extracted accurately and robustly using an adaptive threshold segmentation. The experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate. INDEX TERMS Infrared image sequence, small target detection, local contrast, temporal profile.

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Section: Multi-frame Target Enhancement and Detection Resultsmentioning

confidence: 99%

“…However, it still cannot detect targets with sub-pixel moving velocity well. Liu et al [40] propose a Nonlinear Adaptive Filter (NAF) to extract pulse signals using variance estimation on temporal profiles. NAF is a variant of the median-modified Wiener filter.…”

Section: B Multi-frame Detection Methodsmentioning

confidence: 99%

Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Gao

Lin

2019

IEEE Access

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“…Suggested by the singular value decomposition, a temporal filter was developed for dim target detection in evolving cloud clutters [ 23 ]. A nonlinear adaptive filter was proposed to detect infrared moving dim targets, and has high performance in removing large fluctuations on temporal profiles that are caused by evolving clutters [ 5 ]. By combining spatial and temporal information together, a target detection method was introduced using spatial bilateral filter and temporal cross product, which are respectively used to extract the spatial target information and the features of temporal profiles [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

“…Because of the movement (jitter) of the infrared observation platform or the change of the imaging background, it is difficult to obtain the accurate infrared background by sequential detection methods [ 5 , 6 , 7 ], because the infrared small targets are easily mistaken for background and vice versa. In this case, the single frame detection methods have received a great attention recently, and are valid for infrared small target detection with static or changing backgrounds [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Gao

Wen

Liu

2017

Sensors

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In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.

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“…A gait detection mode [ 15 , 16 , 17 , 18 , 19 ] and adaptive filter [ 20 , 21 , 22 ] have been designed to study the regularity of pedestrian kinematics and walking gaits to offset positioning errors. Integrated positioning systems are introduced to offset errors [ 23 ], including IMU/UWB [ 24 ], IMU/WSN (Wireless Sensor Networks) [ 25 ], INS/WIFI [ 26 ], and INS/RFID [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

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Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

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Moving target detection by nonlinear adaptive filtering on temporal profiles in infrared image sequences

Cited by 38 publications

References 27 publications

Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

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