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The detection and tracking of POIs requires less computation resources.
An interesting approach for the detection and tracking of animals is proposed in [7].
A method for the detection and tracking of animals in wildlife video is proposed by Burghardt and Ćalić [15].
The TBD-based procedures jointly process more consecutive measurements, thus can increase the signal-to-noise ratio (SNR), and realize the detection and tracking of weak targets simultaneously.
Burghardt and Calic [18] worked on the detection and tracking of animal faces based on the Viola-Jones detector and a low-level feature tracker.
We propose a fully automated method for the detection and tracking of elephants in wildlife video which has been collected by biologists in the field.
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The detection and tracking performances of the proposed algorithm are studied with respect to different system settings.
We evaluate the proposed framework on standard datasets in terms of precision and accuracy of the detection and tracking results, and demonstrate the benefits of the integration of prior knowledge in the tracking process.
The consideration of other object features could also help to improve the detection and tracking performance in case of very similar object and background colors.
In panels A and B, step translocation histograms as retrieved by the detection and tracking algorithms on simulated videos of particles following pure Brownian diffusion, with different diffusion coefficients (D = 1 μm/s and D = 10 μm/s for A and B, respectively).
where N SUCCESS stands for the number of SUCCESS point from the detection and tracking, N miss stands for the number of missed points, and N false stands for the number of false alarms.
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