The capability to track individuals in CCTV cameras is important for e.g. surveillance applications at large areas such as train stations, airports and shopping centers. However, it is laborious to track and trace people over multiple cameras. In this paper, we present a system for real-time tracking and fast interactive retrieval of persons in video streams from multiple static surveillance cameras. This system is demonstrated in a shopping mall, where the cameras are positioned without overlapping fields-of-view and have different lighting conditions. The results show that the system allows an operator to find the origin or destination of a person more efficiently. The misses are reduced with 37%, which is a significant improvement.

The capability to track individuals in CCTV cameras is important for surveillance and forensics alike. However, it is laborious to do over multiple cameras. Therefore, an automated system is desirable. In literature several methods have been proposed, but their robustness against varying viewpoints and illumination is limited. Hence performance in realistic settings is also limited. In this paper, we present a novel method for the automatic re-identification of persons in video from surveillance cameras in a realistic setting. The method is computationally efficient, robust to a wide variety of viewpoints and illumination, simple to implement and it requires no training. We compare the performance of our method to several state-of-the-art methods on a publically available dataset that contains the variety of viewpoints and illumination to allow benchmarking. The results indicate that our method shows good performance and enables a human operator to track persons five times faster.

Pollen is a major cause of allergy and monitoring pollen in the air is relevant for diagnostic purposes, development of pollen forecasts, and for biomedical and biological researches. Since counting airborne pollen is a time-consuming task and requires specialized personnel, an automated pollen counting system is desirable. In this article, we present a method for detecting pollen in multifocal optical microscopy images of air samples collected by a Burkard pollen sampler, as a first step in an automated pollen counting procedure. Both color and shape information was used to discriminate pollen grains from other airborne material in the images, such as fungal spores and dirt. A training set of 44 images from successive focal planes (stacks) was used to train the system in recognizing pollen color and for optimization. The performance of the system has been evaluated using a separate set of 17 image stacks containing 65 pollen grains, of which 86% was detected. The obtained precision of 61% can still be increased in the next step of classifying the different pollen in such a counting system. These results show that the detection of pollen is feasible in images from a pollen sampler collecting ambient air. This first step in automated pollen detection may form a reliable basis for an automated pollen counting system.

Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind's Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system.