Camera networks that reconfigure while performing multiple tasks have unique requirements, such as concurrent task allocation with limited resources, the sharing of data among fields of view across the network, and coordination among heterogeneous devices.
Krzysztof Nyka, Lukasz Kulas, and Przemyslaw Woźnica. Sposób lokalizacji osób ilub obiektów wewnatrz ilub na zewnatrz budynków droga komunikacji optycznej i radiowej, zwłaszcza w warunkach wystepowania zakłóceń optycznych oraz układ do realizacji sposobu. Patent (P.408187), Gdansk University of Technology, Poland, 2014.
The complexity of multi-target tracking grows faster than linearly with the increase of the numbers of objects, thus making the design of real-time trackers a challenging task for scenarios with a large number of targets. The Probability Hypothesis Density (PHD) filter is known to help reducing this complexity. However, this reduction may not suffice in critical situations when the number of targets, dimension of the state vector, clutter conditions and sample rate are high. To address this problem, we propose a parallelization scheme for the particle PHD filter. The proposed scheme exploits the knowledge of mutual interacting targets in the scene to help fragmentation and to reduce the workload of individual processors. We compare the proposed approach with alternative parallelization schemes and discuss its advantages and limitations using the results obtained on two multi-target tracking datasets.
We present an extensive survey of methods for recognizing human interactions and propose a method for predicting rendezvous areas in observable and unobservable regions using sparse motion information. Rendezvous areas indicate where people are likely to interact with each other or with static objects (e.g., a door, an information desk or a meeting point). The proposed method infers the direction of movement by calculating prediction lines from displacement vectors and temporally accumulates intersecting locations generated by prediction lines. The intersections are then used as candidate rendezvous areas and modeled as spatial probability density functions using Gaussian Mixture Models. We validate the proposed method to predict dynamic and static rendezvous areas on real-world datasets and compare it with related approaches.