Detection filters and algorithm fusion for ATR

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

Robust infrared small target detection via non-negativity constraint-based sparse representation

Infrared (IR) small target detection is one of the vital techniques in many military applications, including IR remote sensing, early warning, and IR precise guidance. Over-complete dictionary based sparse representation is an effective image representation method that can capture geometrical features of IR small targets by the redundancy of the dictionary. In this paper, we concentrate on solving the problem of robust infrared small target detection under various scenes via sparse representation theory. First, a frequency saliency detection based preprocessing is developed to extract suspected regions that may possibly contain the target so that the subsequent computing load is reduced. Second, a target over-complete dictionary is constructed by a varietal two-dimensional Gaussian model with an extent feature constraint and a background term. Third, a sparse representation model with a non-negativity constraint is proposed for the suspected regions to calculate the corresponding coefficient vectors. Fourth, the detection problem is skillfully converted to an l₁-regularized optimization through an accelerated proximal gradient (APG) method. Finally, based on the distinct sparsity difference, an evaluation index called sparse rate (SR) is presented to extract the real target by an adaptive segmentation directly. Large numbers of experiments demonstrate both the effectiveness and robustness of this method.

Using hidden scale for salient object detection

This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm.

Performance characterization of the dynamic programming obstacle detection algorithm

A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.

Quadratic Gabor filters for object detection

We present a new class of quadratic filters that are capable of creating spherical, elliptical, hyperbolic and linear decision surfaces which result in better detection and classification capabilities than the linear decision surfaces obtained from correlation filters. Each filter comprises of a number of separately designed linear basis filters. These filters are linearly combined into several macro filters; the output from these macro filters are passed through a magnitude square operation and are then linearly combined using real weights to achieve the quadratic decision surface. For detection, the creation of macro filters (linear combinations of multiple single filters) allows for a substantial computational saving by reducing the number of correlation operations required. In this work, we consider the use of Gabor basis filters; the Gabor filter parameters are separately optimized. The fusion parameters to combine the Gabor filter outputs are optimized using an extended piecewise quadratic neural network (E-PQNN). We demonstrate methods for selecting the number of macro Gabor filters, the filter parameters and the linear and nonlinear combination coefficients. We present preliminary results obtained for an infrared (IR) vehicle detection problem.

Improved robustness of target location in nonhomogeneous backgrounds by use of the maximum-likelihood ratio test location algorithm

We address the problem of localizing small targets with random gray levels that appear in random background clutter. We consider the recently proposed maximum-likelihood ratio test (MLRT) algorithm, which scans the observed scene with an estimation window in which the local statistics are estimated. In the presence of a spatially homogeneous background, we show that if the estimation window is a few times larger than the target itself, the MLRT is quasi-equivalent to the optimal maximum-likelihood (ML) algorithm, which uses the whole scene for estimating the background statistics. The MLRT thus constitutes an efficient alternative to the ML algorithm and is more robust in dealing with spatially nonhomogeneous clutter since it utilizes a small estimation window.