Tracking of motor vehicles from aerial video imagery using the OT-MACH correlation filter

Accurately tracking moving targets in a complex scene involving moving cameras, occlusions and targets embedded in noise is a very active research area in computer vision. In this paper, an optimal trade-off maximum correlation height (OT-MACH) filter has been designed and implemented as a robust tracker. The algorithm allows selection of different objects as a target, based on the operator’s requirements. The user interface is designed so as to allow the selection of a different target for tracking at any time. The filter is updated, at a frequency selected by the user, which makes the filter more resistant to progressive changes in the object’s orientation and scale. The tracker has been tested on both colour visible band as well as infra-red band video sequences acquired from the air by the Sussex County police helicopter. Initial testing has demonstrated the ability of the filter to maintain a stable track on vehicles despite changes of scale, orientation and lighting and the ability to re-acquire the track after short losses due to the vehicle passing behind occlusions

Gravity optimised particle filter for hand tracking

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Microscopy Cell Segmentation via Adversarial Neural Networks

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.gitComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 201

Correlation of chlorophyll, suspended matter, and related parameters of waters in the lower Chesapeake Bay area to LANDSAT-1 imagery

The author has identified the following significant results. An effort to relate water parameters of the lower Chesapeake Bay area to multispectral scanner images of LANDSAT 1 has shown that some spectral bands can be correlated to water parameters, and has demonstrated the feasibility of synoptic mapping of estuaries by satellite. Bands 5 and 6 were shown to be useful for monitoring total particles. Band 5 showed high correlation with suspended sediment concentration. Attenuation coefficients monitored continuously by ship along three baselines were cross correlated with radiance values on three days. Improved correlations resulted when tidal conditions were taken into consideration. A contouring program was developed to display sediment variation in the lower Chesapeake Bay from the MSS bands