Small Moving Target Detection Research Articles

High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

Method of Infrared Small Moving Target Detection Based on Coarse-to-Fine Structure in Complex Scenes

In the combat system, infrared target detection is an important issue worthy of study. However, due to the small size of the target in the infrared image, the low signal-to-noise ratio of the image and the uncertainty of motion, how to detect the target accurately and quickly is still difficult. Therefore, in this paper, an infrared method of detecting small moving targets based on a coarse-to-fine structure (MCFS) is proposed. The algorithm mainly consists of three modules. The potential target extraction module first smoothes the image through a Laplacian filter and extracts the prior weight of the image by the proposed weighted harmonic method to enhance the target and suppress the background. Then, the local variance feature map and local contrast feature map of the image are calculated through a multiscale three-layer window to obtain the potential target region. Next, a new robust region intensity level (RRIL) algorithm is proposed in the spatial-domain weighting module. Finally, the temporal-domain weighting module is established to enhance the target positions by analyzing the kurtosis features of temporal signals. Experiments are conducted on real infrared datasets. Through scientific analysis, the proposed method can successfully detect the target, at the same time, the ability to suppress the background and the ability to improve the target has reached the maximum, which verifies the effectiveness of the algorithm.

Research on small moving target detection algorithm based on complex scene

In recent years, small moving target detection based on complex scenes is widely used in many fields, such as computer vision, image processing and pattern recognition. It is widely used in military navigation, intelligent video monitoring system, video retrieval and other fields. Due to the dynamic background in the complex scene, the moving object has the characteristics of long distance, small area, unclear texture and color features, and partial loss of detail features. Therefore, how to extract small moving objects quickly, accurately and completely from complex scenes is the key of subsequent image analysis and processing. Aiming at the problem of strong background noise in more complex scenes, this paper proposes a neighborhood frame search denoising algorithm based on time consistency, which can effectively remove the strong background noise disturbance. The experimental results show that the algorithm can effectively remove strong background noise.

Bio-Inspired Video Enhancement for Small Moving Target Detection.

Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.

Enhancing Small Moving Target Detection Performance in Low-Quality and Long-Range Infrared Videos Using Optical Flow Techniques

The detection of small moving objects in long-range infrared videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we summarize the investigation of efficient ways to enhance the performance of small target detection in long-range and low-quality infrared videos containing moving objects. In particular, we focus on unsupervised, modular, flexible, and efficient methods for target detection performance enhancement using motion information extracted from optical flow methods. Three well-known optical flow methods were studied. It was found that optical flow methods need to be combined with contrast enhancement, connected component analysis, and target association in order to be effective for target detection. Extensive experiments using long-range mid-wave infrared (MWIR) videos from the Defense Systems Information Analysis Center (DSIAC) dataset clearly demonstrated the efficacy of our proposed approach.

A bio-inspired spatiotemporal contrast operator for small and low-heat-signature target detection in infrared imagery

Thermal infrared imaging is a promising modality for long-range small target detection. However, low target contrast, high background clutter and sensor noise are some of the key challenges that need to be resolved efficiently for robust detection performance. The spatiotemporal processing in the early stages of the visual pathway of small flying insects has a remarkable ability to simultaneously address such challenges. The first stage of the early visual system corresponds to the adaptive temporal filtering mechanisms of photoreceptor cells. This stage improves the signal-to-noise ratio, enhances target background discrimination and compresses the signal bandwidth. The second stage pertains to the adaptive spatiotemporal filtering in the lamina monopolar cells. This stage removes spatiotemporal redundancy and enhances target contrast. In this paper, we explore such two-stage bio-processing to simultaneously suppress clutter and enhance the contrast of small low-heat-signature targets in real-world infrared imagery. We also propose a simple and efficient spatial contrast operator called center–surround total differential index for target region segmentation. Small moving target detection experiments on real-world high-bit-depth infrared video sequences show that the proposed method significantly outperforms the state-of-the-art spatial and spatiotemporal infrared small target detection methods. Specifically, our method resulted in 59% better detection rate (at 10 $$^{-5}$$ false alarm rate) than the best competing method. Our results show that the bio-inspired spatiotemporal preprocessing is an excellent tool for significantly improving the performance of existing long-range infrared target detection techniques.

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

In order to solve the problem of weak target detection in complex moving background, a joint algorithm model of space-time domain is proposed in this paper. The algorithm first selects appropriate length of the time domain window and loop step size. In each time domain window: (1) In time domain processing stage, the algorithm fully considers the factors of background movement, and uses neighborhood similarity measure based on Pearson correlation coefficient to suppress most part of background area. (2) In space domain processing stage, a target detection method based on regional gray level (RGL) is proposed to suppress residual background and re-enhance the target. In each iteration, this joint detection model can obtain part of the trajectory of the target in real time. Therefore, after all iterations are completed (all images in the sequence have been processed), the target can be detected and the target trajectory can be efficiently extracted. The experimental results in this paper show that the proposed algorithm can effectively detect targets with maximum speed of 5 pixel/frame in the image sequence with average SCR ≤ 1 and background moving speed ≤0.8 pixel/frame.

Improved small moving target detection method in infrared sequences under a rotational background.

The infrared image sequence contains both the rotational background and a moving target, causing great difficulties in target detection and the occurrence of massive false alarm rates. This paper proposes an improved method using an optical flow field, which can estimate and compensate for the rotational motion background, thus detecting a small moving target. First of all, we select the Shi-Tomasi angular points for image detection and represent the background rotation with the sparse optical flow field of a complete image. Then we use the least squares method to estimate the parameters of the perspective model of background rotation so as to reconstruct the background motion vector field and compensate for background rotation. Finally we detect the small moving target with the differential method and the morphological treatment. The experimental results verify that this method can accurately detect a small moving target against the violently rotational background and provide an idea for the target interception algorithm under the condition that an infrared imaging seeker is overlooking.

Small Moving Target Detection Model Based on Improved Mixed Disturbance Gauss Model

In order to obtain ideal moving target detection results, a kind of moving target detection algorithm of improved mixture Gaussian model is proposed in this paper on the basis of analyzing the deficiency of traditional Mixture Gaussian model. Firstly, in this algorithm, the update rate is increased to better adapt to the change of environment and the idea of pixel connected region is introduced to remove the noise, then the invariance property of chroma is adopted to avoid arising of “shadow” phenomenon; finally, simulation comparison experiment is used to test its performance. The experiment results show that this algorithm can generate reliable background image and accurately and completely detect the moving target to obtain more superior detection results of comparison algorithm, which can meet the practical application requirement of target tracking.

Infrared small moving target detection using sparse representation-based image decomposition

Infrared small moving target detection is one of the crucial techniques in infrared search and tracking systems. This paper presents a novel small moving target detection method for infrared image sequence with complicated background. The key points are given as follows: (1) since target detection mainly depends on the incoherence between target and background, the proposed method separate the target from the background according to the morphological feature diversity between target and background; (2) considering the continuity of target motion in time domain, the target trajectory is extracted by the RX filter in random projection. The experiments on various clutter background sequences have validated the detection capability of the proposed method. The experimental results show that the proposed method can robustly provide a higher detection probability and a lower false alarm rate than baseline methods.

Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery

A dim small moving target detection algorithm based on joint spatio-temporal sparse recovery is proposed in this paper. A spatio-temporal over-complete dictionary is firstly trained from infrared image sequence, and it can characterize not only motion information but also morphological feature. In the spatio-temporal over-complete dictionary, the spatio-temporal atom is then classified as target spatio-temporal atom building target spatio-temporal over-complete dictionary, which describes moving target, and background spatio-temporal atom constructing background spatio-temporal over-complete dictionary, which embeds background clutter. Infrared image sequence is decomposed on the union of target spatio-temporal over-complete dictionary and background spatio-temporal over-complete dictionary. The residual reconstructed by its homologous spatio-temporal over-complete dictionary is very little, yet the residual recovered by its heterogonous spatio-temporal over-complete dictionary is quite large. Therefore, their residuals after decomposing and reconstruction by the joint spatio-temporal sparse recovery would differ so distinctly that it is adopted to decide the signal is from target or background. Some experiments are induced and the experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively.

Small Target Detection based on Infrared Image Adaptive

Abstract This paper studies the multi-resolution analysis of infrared image preprocessing method based on wavelet transform, wavelet transform infrared image of small target for pretreatment, after pretreatment suppressed image background clutter, improved signal to noise ratio. On this basis, studies based on infrared image sequences generated background Kalman filter and target detection algorithm, given the principle of the algorithm, time-domain method for generating fading background and moving object extraction methods such as recursive least squares method, and the experimental results show that the algorithm capable of detecting small targets in infrared images, with good results. based pretreatment small target motion continuity characteristics studied image sequences small moving target detection algorithm based on weighted dynamic programming, dynamic programming algorithm principle is given, analysis of the direct method and the accumulation of gray dynamic programming algorithm based on similar likelihood function for dynamic programming problems arise energy diffusion gives improved dynamic programming algorithm: weighted dynamic programming algorithm, the algorithm implementation steps, testing and experimental structure the results were analyzed, the results show that the algorithm for small moving target detection with good results. Finally, this paper studied the infrared image reject false target trajectory get the real target trajectory correlation method, so as to improve the detection rate and reduce the false alarm rate the purpose.

A Method of Small Moving Target Detection

This paper present a new method for detecting small moving targets from sequential images under the complex background of sky. The method can identify moving targets in low SNR, estimate and track the trajectory of the target using the maximum gray matrix and Kalman filter theory. Compared with other algorithms, this method has features of small amount of calculation, fast detecting speed, high real-time performance, and small identification error. Experimental results show that the pepper noise and fixed pattern noise in this type of image sequence have a good filtering effect, and also our method has good recognition and tracking effect for the small moving targets. Copyright c 2014 Binary Information Press.

A novel spatial–temporal detection method of dim infrared moving small target

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial–temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time–space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

A novel infrared small moving target detection method based on tracking interest points under complicated background

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

Infrared small moving target detection method based on graph matching

We apply graph matching method to detect infrared small moving targets using image sequences. Candidates (interest points) detected in the first frame form one graph and the same candidates in the last frame form another one. The real moving targets are extracted by matching these two graphs. Experimental results demonstrate that the proposed method is robust and efficient to the translation and rotation of the background.

Small Moving Target Detection based on the Concept of Entropy Flow

The detection of small moving targets under motorial background is very difficult. In order to solve this issue, a novel detection algorithm is presented in this paper, which is based on the concept of entropy flow. Firstly, the concept of entropy flow is used to measure image motion; Secondly, 2-dimensional histogram of flow field is applied to estimate the background motion; Finally, the targets are obtained by using pipeline filtering in difference image. The experiment results demonstrate that the proposed algorithm is appropriate to detect small targets under moving backgrounds, and the algorithm provides an instructive approach to the detection of small targets.

Temporal Profile Based Small Moving Target Detection Algorithm in Infrared Image Sequences

A new algorithm is presented which deals with the problem of detecting small moving targets in infrared image sequences that also contain drifting and evolving clutter. Through development of models of the temporal behavior of the static background, target and cloud edge on a single pixel basis, the new algorithm employing the connecting line of the stagnation points (CLSP) of the temporal profile as the baseline is created and tested. The deviation of the temporal profile and its CLSP is analyzed and it is determined that the distribution of the residual temporal profile obtained by subtracting the baseline from the temporal profile can be modeled by a Gaussian distribution. The occurrences of the targets have intensity values significantly different to the distribution of the residual temporal profile. Unlike the conventional 3-D method, this new algorithm operates on the temporal profile in 1-D space, not in 3-D space, thus having a higher computational efficiency. Experiments with real IR image sequences have proved the validity of the new approach.