Dim small moving target detection and tracking method based on spatial-temporal joint processing model

Lv, Ping-Yue; Lin, Cai; Sun, Songlin

doi:10.1016/j.infrared.2019.102973

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…Li et al [31] enhanced the small infrared target via saliency analysis based on motion and appearance. Lv et al [32] suppressed the highly correlated backgrounds within successive frames and then detected the target based on local gray distribution. Ren et al [33] treated the small infrared target as noise and detected it using a denoising algorithm.…”

Section: ) Image Patch Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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Section: ) Image Patch Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…The value of l was set to two after parameter analysis conducted in Section 4. To evaluate the detection effectiveness, LSMs are compared with seven state-of-the-art detection methods, including fusion saliency map (FSM) [10], double-neighborhood gradient method (DNGM) [11], neighborhood saliency map (NSM) [14], spatial-temporal local contrast filter (STLCF) [15], spatial-temporal local contrast method (STLCM) [2], spatialtemporal joint processing model (STJP) [28], and multiscale local target characteristics algorithm (MLTC) [29]. NSM, STLCF, STLCM, and STJP are existing space-based detection methods, FSM is a newly proposed detection method utilized for low-altitude slow target detection that has a similar background to space-based detection, and DNGM and MLTC are new detection methods proposed in 2020 and 2021, respectively.…”

Section: Experimental Condition and Evaluation Indexmentioning

confidence: 99%

“…Lv et al proposed a method that detects the space-based weak moving target with an SCR ≈ 1 or even <1 [27]. They further proposed a dim small moving target detection and tracking method based on a spatial-temporal joint processing model (STJP) [28], which also performed well on spacebased dim target detection. However, the existing space-based detection methods mainly focus on dim target enhancement but ignore interference resulting from the strong clutter.…”

Section: Introductionmentioning

confidence: 99%

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Chen

Rao

Chen

et al. 2022

Sensors

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The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

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Infrared moving small target detection based on spatial-temporal local contrast under slow-moving cloud background

Xi,

Zhou,

Jiang

et al. 2023

Infrared Physics & Technology

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Dim small moving target detection and tracking method based on spatial-temporal joint processing model

Cited by 8 publications

References 21 publications

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Infrared moving small target detection based on spatial-temporal local contrast under slow-moving cloud background

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