Automatic detection and tracking of persons and vehicles can greatly increase situational awareness in many military applications. Various methods for detection and tracking have been proposed so far, both for rule-based and learning approaches. With the advent of deep learning, learning approaches generally outperform rule-based approaches. Pre-trained neural networks on datasets like MS COCO can give reasonable detection performance on military datasets. However, for optimal performance it is advised to optimize the training of these pre-trained networks with a representative dataset. In typical military settings, it is a challenge to acquire enough data, and to split the training and test set properly. In this paper we evaluate fine-tuning on military data and compare different pre- and post-processing methods. First we compare a standard pre-trained RetinaNet detector with a fine-tuned version, trained on similar objects, which are recorded at distances different than the distance in the test set. On the aspect of distance this train set is therefore out-of-distribution. Next, we augment the training examples by both increasing and decreasing their size. Once detected, we use a template tracker to follow the objects, compensating for any missing detections. We show the results on detection and tracking of persons and vehicles in visible imagery in a military long range detection setting. The results show the added value of fine-tuning a neural net with augmented examples, where final network performance is similar to human visual performance for detection of targets, with a target area of tens of pixels in a moderately cluttered land environment.
Imaging systems can be used to obtain situational awareness in maritime situations. Important tools for these systems are automatic detection and tracking of objects in the acquired imagery, in which numerous methods are being developed. When designing a detection or tracking algorithm, its quality should be ensured by a comparison with existing algorithms and/or with a ground truth. Detection and tracking methods are often designed for a specific task, so evaluation with respect to this task is crucial, which demands for different evaluation measures for different tasks. We, therefore, propose a variety of quantitative measures for the performance evaluation of detectors and trackers for a variety of tasks. The proposed measures are a rich set from which an algorithm designer can choose in order to optimally design and assess a detection or tracking algorithm for a specific task. We compare these different evaluation measures by using them to assess detection and tracking quality in different maritime detection and tracking situations, obtained from three real-life infrared video data sets. With the proposed set of evaluation measures, a user is able to quantitatively assess the performance of a detector or tracker, which enables an optimal design for his approach.
Detecting maritime targets with electro-optical (EO) sensors is an active area of research. One current trend is to automate target detection through image processing or computer vision. Automation of target detection will decrease the number of people required for lower-level tasks, which frees capacity for higher-level tasks. A second trend is that the targets of interest are changing; more distributed and smaller targets are of increasing interest. Technological trends enable combined detection and identification of targets through machine learning. These trends and new technologies require a new approach in target detection strategies with specific attention to choosing which sensors and platforms to deploy.
In our current research, we propose a ‘maritime detection framework 2.0’, in which multi-platform sensors are combined with detection algorithms. In this paper, we present a comparison of detection algorithms for EO sensors within our developed framework and quantify the performance of this framework on representative data.
Automatic detection can be performed within the proposed framework in three ways: 1) using existing detectors, such as detectors based on movement or local intensities; 2) using a newly developed detector based on saliency on the scene level; and 3) using a state-of-the-art deep learning method. After detection, false alarms are suppressed using consecutive tracking approaches. The performance of these detection methods is compared by evaluating the detection probability versus the false alarm rate for realistic multi-sensor data.
New types of maritime targets require new target detection strategies. Combining new detection strategies with existing tracking technologies shows potential increase in detection performance of the complete framework.
Both normal aging and neurodegenerative diseases such as Alzheimer’s disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.