Image Pre-processing Algorithms for Detection of Small/Point Airborne Targets

The problem of detecting small/point targets in infra-red imagery is an important research area for defence applications. The challenge is to achieve high sensitivity for detection of dim point like small targets with low false alarms and high detection probability. To detect the target in such scenario, pre-processing algorithms are used to predict the complex background and then to subtract predicted background from the original image. The difference image is passed to the detection algorithm to further distinguish between target and background and/or noise. The aim of the study is to fit the background as closely as possible in the original image without diminishing the target signal. A number of pre-processing algorithms (spatial, temporal and spatio-temporal) have been reported in the literature. In this paper a survey of different pre-processing algorithm is presented. An improved hybrid morphological filter, which provides high gain in signal-to-noise plus clutter ratio (SCNR), has been proposed for detection of small/point targets.Defence Science Journal, 2009, 59(2), pp.166-174, DOI:http://dx.doi.org/10.14429/dsj.59.150

Airborne Infrared Search and Track Systems

Infrared search and track (IRST) systems are required for fighter aircraft to enable them to passively search, detect, track, classify, and prioritise multiple airborne targets under all aspects, look-up, look-down, and co-altitude conditions and engage them at as long ranges as possible. While the IRST systems have been proven in performance for ground-based and naval-based platforms, it is still facing some technical problems for airborne applications. These problems arise from uncertainty in target signature, atmospheric effects, background clutter (especially dense and varying clouds), signal and data processing algorithms to detect potential targets at long ranges and some hardware limitations such as large memory requirement to store and process wide field of view data. In this paper, an overview of airborne IRST as a system has been presented with detailed comparative simulation results of different detectionitracking algorithms and the present status of airborne IRST

An Adaptive Spatial-Temporal Local Feature Difference Method for Infrared Small-moving Target Detection

Detecting small moving targets accurately in infrared (IR) image sequences is a significant challenge. To address this problem, we propose a novel method called spatial-temporal local feature difference (STLFD) with adaptive background suppression (ABS). Our approach utilizes filters in the spatial and temporal domains and performs pixel-level ABS on the output to enhance the contrast between the target and the background. The proposed method comprises three steps. First, we obtain three temporal frame images based on the current frame image and extract two feature maps using the designed spatial domain and temporal domain filters. Next, we fuse the information of the spatial domain and temporal domain to produce the spatial-temporal feature maps and suppress noise using our pixel-level ABS module. Finally, we obtain the segmented binary map by applying a threshold. Our experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods for infrared small-moving target detection

A flexible algorithm for detecting challenging moving objects in real-time within IR video sequences

Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City

Life-like Image Processing for Small Target Motion Detection in Cluttered Dynamic Environments

Discriminating targets moving against a cluttered background is a huge challenge for future robotic vision systems, let alone detecting a target as small as one or a few pixels. As a source of inspiration, insects are quite apt at searching for mates and tracking prey – which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Some of the STMDs have also demonstrated direction selectivity which means these STMDs respond strongly only to their preferred motion direction. Build a quantitative STMD model is the first step for not only further understanding of the biological visual system, but also providing robust and economic solutions of small target detection for an artificial visual system. This research aims to explore STMD-based image processing methods for small target motion detection against cluttered dynamic backgrounds. The major contributions are summarized as follows. Three STMD-based neural models are proposed in this research named as directionally selective STMD(DSTMD), STMD Plus and Feedback STMD, respectively. The DSTMD systematically models and studies direction selectivity of the STMD neurons, meanwhile provides with unified and rigorous mathematical description. Specifically, in the DSTMD, a new correlation mechanism is introduced for direction selectivity via correlating signals relayed from two pixels. Then, a lateral inhibition mechanism is implemented on the spatial field for size selectivity of the STMD neurons. Finally, a population vector algorithm is used to encode motion direction of small targets. Extensive experiments showed that the proposed DSTMD not only is in accord with current biological findings, i.e. showing directional preferences, but also works reliably in detecting small targets against cluttered backgrounds. The STMD Plus is developed to discriminate small targets from small-target-like background features (named as fake features) by integrating motion information with directional contrast. More precisely, the STMD Plus is composed of four subsystems – ommatidia, motion pathway, contrast pathway and mushroom body. Compared to existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated in the mushroom body for small target discrimination. The experimental results demonstrated the significant and consistent improvements of the proposed visual system model over existing STMD-based models against fake features. The Feedback STMD is also designed to filter out fake features by introducing a new feedback mechanism. Specifically, the model output is first temporally delayed then applied to the previous neural layer to construct a feedback loop. By subtracting the feedback signal from the inputs of the STMDs, the background fake features are largely suppressed. Experimental results show that the developed feedback neural model achieves better performance than the existing STMD-based models in discriminating small targets from complex backgrounds

A Comparative Evaluation of the Detection and Tracking Capability Between Novel Event-Based and Conventional Frame-Based Sensors

Traditional frame-based technology continues to suffer from motion blur, low dynamic range, speed limitations and high data storage requirements. Event-based sensors offer a potential solution to these challenges. This research centers around a comparative assessment of frame and event-based object detection and tracking. A basic frame-based algorithm is used to compare against two different event-based algorithms. First event-based pseudo-frames were parsed through standard frame-based algorithms and secondly, target tracks were constructed directly from filtered events. The findings show there is significant value in pursuing the technology further