Infrared Surveillance Systems Research Articles

Infrared Small Target Detection Based on Multidirectional Cumulative Measure

Robustness of small target detection is a researchable hotspot in infrared surveillance system. The residual phenomenon of background clutter is universal in current local comparison methods. Algorithm of sparse low-rank decomposition restoration cannot be applied to the actual situations due to the long time consumption. This letter proposes a multi-directional cumulative measure (MDCM) to enhance saliency and effectiveness of weak-small target detection. Firstly,multi-directional cumulative mean difference is implemented in central layer and background layer to estimate the background, while multi-directional cumulative derivative multiplying is calculated in central-active layer to characterize overall target’s heterogeneity, then technology of image fusion is adopted to eliminate interference of false target. Finally,a simple adjudicative technology is employed toward separated target region from complex scenes. Compared to up to date existing approaches, extensive simulational testing on four public datasets prove that proposed approach is capable of separating small targets efficiently from an irregular background in a single-scale window and achieve a comparable or even better accuracy.

A Multimodal Fusion-Based LNG Detection for Monitoring Energy Facilities (Student Abstract)

Fossil energy products such as liquefied natural gas (LNG) are among Canada's most important exports. Canadian engineers devote themselves to constructing visual surveillance systems for detecting potential LNG emissions in energy facilities. Beyond the previous infrared (IR) surveillance system, in this paper, a multimodal fusion-based LNG detection (MFLNGD) framework is proposed to enhance the detection quality by the integration of IR and visible (VI) cameras. Besides, a Fourier transformer is developed to fuse IR and VI features better. The experimental results suggest the effectiveness of the proposed framework.

Scale-space point spread function based framework to boost infrared target detection algorithms

Small target detection is one of the major concern in the development of infrared surveillance systems. Detection algorithms based on Gaussian target modeling have attracted most attention from researchers in this field. However, the lack of accurate target modeling limits the performance of this type of infrared small target detection algorithms. In this paper, signal to clutter ratio (SCR) improvement mechanism based on the matched filter is described in detail and effect of Point Spread Function (PSF) on the intensity and spatial distribution of the target pixels is clarified comprehensively. In the following, a new parametric model for small infrared targets is developed based on the PSF of imaging system which can be considered as a matched filter. Based on this model, a new framework to boost model-based infrared target detection algorithms is presented. In order to show the performance of this new framework, the proposed model is adopted in Laplacian scale-space algorithms which is a well-known algorithm in the small infrared target detection field. Simulation results show that the proposed framework has better detection performance in comparison with the Gaussian one and improves the overall performance of IRST system. By analyzing the performance of the proposed algorithm based on this new framework in a quantitative manner, this new framework shows at least 20% improvement in the output SCR values in comparison with Laplacian of Gaussian (LoG) algorithm.

Classification of Captive North American River Otters (Lontra canadensis) Vocal Repertoires: Individual Variations, and Age Class Comparisons

This is the first study to examine in detail the vocal behaviors of North American river otters (Lontra canadensis), and the results suggest that river otters have complex vocal repertoires comprised of four distinct vocal types and seven sub-call types. The vocalizations and behaviors of ten captive North American river otter adults, one litter of newborn pups and one litter of pups at eight weeks old were recorded using a SONY Handheld DV camera and an infrared surveillance system. A quantitative analysis of 2726 calls on the adults and 299 calls for the pups was conducted for acoustic parameters that included frequencies, powers, and duration. Whine, chirp and chatter call types were the main vocal elements of the vocal repertoire and were present at birth. Pups vocals were structurally underdeveloped versions of the adult vocals and adults call types showed individual variations. This suggests that vocalizations are likely individually modified as pups enter adulthood. A unique whistle was present in newborn pup vocal repertoires but appeared to be reduced in the repertoire by eight weeks old. However, further research needs to be conducted to determine the function of the whistle.

Non-uniformity correction of infrared focal plane array in point target surveillance systems

We discuss the influence of non-uniformity and non-uniformity correction on point target detection in infrared surveillance system, and propose a non-uniformity correction approach which is based on signal intensity and sensor characteristics. Theoretical models are used to derive the combined effect of background clutter, sensor random noise, target, non-uniformity and correction error on the signal-to-noise-and-clutter ratio. From our analysis, it can be noted that background clutter intensity is successively modulated by sensor non-uniformity and non-uniformity correction, while sensor random noise is modulated by the non-uniformity correction process only. Furthermore, background clutter and sensor random noise are the key factors that affect the performance of a surveillance system, when it is used to detect point targets. The method presented in this paper takes all of the above into account, moreover, it considers the difference between scanning and staring focal plane array. The experimental results demonstrate the effectiveness of the proposed method.

The automated infrared thermal imaging system for the continuous long-term monitoring of the surface temperature of the Vesuvius crater

<p>Infrared remote sensing monitoring is a significant tool aimed to integrated surveillance system of active volcanic areas. In this paper we describe the realization and the technological evolution of the permanent image thermal infrared (TIR) surveillance system of the Vesuvius volcano. The TIR monitoring station was installed on the Vesuvius crater rim on July 2004 in order to acquire scenes of the SW inner slope of Vesuvius crater that is characterized by a significant thermal emission. At that time, it represented the first achievement all over the world of a permanent surveillance thermal imaging system on a volcano. It has been working in its prototypal configuration till May 2007. The experience gained over years about the engineering, management and maintenance of TIR remote acquisition systems in extreme environmental conditions, allows us to design and realize a new release of the TIR monitoring station with improved functionalities and more flexibility for the IR image acquisition, management and storage, which became operational in June 2011. In order to characterize the thermal background of the Vesuvius crater at present state of volcanic quiescence, the time series of TIR images gathered between July 2004 and May 2012 were analyzed using a statistical approach. Results show no significant changes in the thermal radiation during the observation periods, so they can be assumed as representative of a background level to which refer for the interpretation of possible future anomalies related to a renewal of the volcanic dynamics of the Vesuvius volcano.</p>

Background subtraction based level sets for human segmentation in thermal infrared surveillance systems

Based on the technique of background subtraction, two level set based active contour models (LSACs) named as RT-BSLSAC and EA-BSLSAC are proposed for human segmentation in thermal infrared surveillance systems. The energy functional of RT-BSLSAC is initially formulated with the spatial–temporal information extracted from the background-subtracted images that correspond to the current frame and its adjacent frames. Then, minimization of such functional is conducted by a real-time numeric scheme evolving a binary level set function (BLSF). When the BLSF converges, the moving humans in current frame are detected with relatively complete interiors and enclosed, smooth contours. EA-BSLSAC makes two improvements to RT-BSLSAC. First, the formulation of energy functional not only depends on spatial–temporal information but also the boundary information resulting from an edge detector. Second, the functional is minimized by a convex numeric scheme featured by initialization-invariance. As a result, EA-BSLSAC presents higher segmentation accuracy but at more computational cost in comparison with RT-BSLSAC. Experimental results from segmenting the real-world infrared surveillance clips validate the advantages of the proposed methods in accuracy, efficiency, and the coordination with other algorithmic components of an infrared surveillance system due to the cancellation of post-processing meaning to reach complete human interiors and exact silhouettes.

Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems

A well-established scheme for target detection in infrared (IR) surveillance systems consists of applying a suitable decision rule on the images with background clutter previously removed. Background removal is accomplished by subtracting, from the original image, the estimate of the spatially varying background signal obtained by a background estimation algorithm (BEA). The overall target detection performance is strongly influenced by the effectiveness of the employed BEA. Particularly, the BEA and its design parameters should be chosen so as to get an accurate estimate of the background signal and to avoid biases caused by the possible presence of targets (target leakage). In this work, we present a novel method for the choice and setting of the best performing background removal technique for the detection of dim point targets. The proposed procedure is based on the simulation of dim targets implanted on an acquired sample image representing the scenario of interest. The choice of the best performing BEA is made by exploring the performance of the detection scheme for several configurations of the characteristic parameters of the BEAs. The effectiveness of the BEA selection procedure is evaluated in two case studies where real image sequences acquired by IR cameras are employed. The results confirm the benefits introduced by the proposed technique. Indeed, the performance of the IR detection system with the BEA tuned according to the proposed selection criterion is improved in that the number of false alarms is reduced up to 2 orders of magnitude compared with BEAs in other common configurations.

Comparative analysis of clutter removal techniques over experimental IR images

Infrared surveillance systems have the task of detecting small moving targets having low signal-to-clutter ratio. Detection is usually accomplished by (1) removing the background structures from each frame and (2) integrating the target signal over consecutive frames of the residual sequence. We focus on the analysis of background removal techniques based on linear and nonlinear two-dimensional filters such as the window average, median, max-median, and max-mean. We introduce two modified versions of the window average and max-mean filters, where an appropriate guard window is used to reduce the bias due to the target. We define an ad hoc methodology to compare the different background estimation techniques on the basis of their ability to suppress background structures and to preserve the target of interest. Finally, we present and discuss the results obtained over two experimental IR sequences containing a highly structured background.

Reduction of false alarm rate in automatic forest fire infrared surveillance systems

One of the main problems related to infrared remote sensing of forest fires by automatic systems concerns the rejection of false alarms. The study of the infrared spectral radiance emitted by a biomass fire has been used to define spectral algorithms that permit to separate fires from other sources considered as false alarms. The different behaviour of the medium (3–5 μm) and thermal (8–12 μm) infrared spectral regions has been the basis for the definition of a new “figure” of merit, the so-called Fire Index (FI). An experimental burn has been used to test the ability of FI to identify clearly fires. A second algorithm based on an intraband analysis, the Mid-IR Fire Index (MFI), has been proposed to give complementary information to FI. Evolution of FI and MFI values with distance for different IR sources has been calculated. Finally, an experimental validation of both algorithms has been performed by using two infrared cameras working in the medium and thermal infrared spectral windows.

Comparative Performance Analysis of the MWIR and LWIR Focal Plane Array Starring Imaging Infrared Systems

This paper review the fundamental physics of IR radiation and describes the predictive techniques and the methods of signature management for the specific exploitation of Imaging Infrared Surveillance Systems for shore based or ship borne surface to surface and low altitude air to surface scenarios, which are the most widely used. The parameters affecting the performance of the FLIR systems, such as target and background characteristics, atmospheric transmission and detector features are individually analyzed, in order to reach to FLIR performance assessment using either MWIR or LWIR bands. For this purpose the special scenario of maritime applications has been paying special consideration.

Motion estimation methods and noisy phenomena

In an infrared surveillance system (which must detect remote sources and thus has a very low resolution) in an aerospace environment, the estimation of the cloudy sky velocity should lower the false alarm rate in discriminating the motion between various moving shapes by means of a background velocity map. The optical flow constraint equation, based on a Taylor expansion of the intensity function, is often used to estimate the motion for each pixel. One of the main problems in motion estimation is that, for one pixel, the real velocity cannot be found because of the aperture problem. Another kinematic estimation method is based on a matched filter [generalized Hough transform (GHT)]: it gives a global velocity estimation for a set of pixels. On the one hand we obtain a local velocity estimation for each pixel with little credibility because the optical flow is so sensitivity to noise; on the other hand, we obtain a robust global kinematic estimation, the same for all selected pixels. This paper aims to adapt and improve the GHT in our typical application in which one must discern the global movement of objects (clouds), whatever their form may be (clouds with hazy edges or distorted shapes or even clouds that have very little structure). We propose an improvement of the GHT algorithm by segmentation images with polar constraints on spatial gradients. One pixel, at timet, is matched with another one at timet + ΔT, only if the direction and modulus of the gradient are similar. This technique, which is very efficient, sharpens the peak and improves the motion resolution. Each of these estimations is calculated within windows belonging to the image, these windows being selected by means of an entropy criterion. The kinematic vector is computed accurately by means of the optical flow constraint equation applied on the displaced window. We showed that, for small displacements, the optical flow constraint equation sharpens the results of the GHT. Thus a semi-dense velocity field is obtained for cloud edges. A velocity map computed on real sequences with these methods is shown. In this way, a kinematic parameter discriminates between a target and the cloudy background.

Multiple hypothesis track confirmation for infrared surveillance systems

An overall methodology is described for the application of a multiple hypothesis tracking (MHT) algorithm to the infrared (IR) surveillance system problem of establishing tracks on dim targets in a heavy clutter or false alarm background. The authors discuss the manner in which the detection and tracking systems are jointly designed to optimize performance. They present approximate methods that can conveniently be used for preliminary system design and performance prediction. They discuss the use of a detailed Monte Carlo simulation for final system evaluation and present results illustrating the proposed methods and comparing predicted and simulation performance. >

Sophisticated detector design for increased infrared sensitivity

The potential for improving the performance of infrared surveillance systems by incorporating sophisticated detector design is discussed herein. This potential lies in improved background discrimination and incident energy utilization. Progress has been made toward realizing such improvements as the result of thin-film techniques. The principle involved in the discrimination technique is analogous to the bridge-type bolometer circuit used to cancel out ambients. This principle has been implemented by transforming these bridge circuits into elements of microscopic dimensions. The distributed network and high resolution make this concept conducive to thin-film circuitry. Material problems encountered such as the compatibility of dielectric and photoconductor are discussed with recommendations for further research.