Evaluation of trackers for Pan-Tilt-Zoom Scenarios
This work addresses the challenge of reliable tracking in PTZ scenarios for computer vision applications, but it is incremental as it focuses on evaluation and prediction within an existing framework.
The paper tackled the problem of evaluating tracking algorithms for Pan-Tilt-Zoom (PTZ) cameras, where standard benchmarks are inadequate due to dynamic capture conditions and camera control needs, by using a virtual PTZ framework to assess performance and extending it with target position prediction to improve robustness.
Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. Compared to tracking with a still camera, the images captured with a PTZ camera are highly dynamic in nature because the camera can perform large motion resulting in quickly changing capture conditions. Furthermore, tracking with a PTZ camera involves camera control to position the camera on the target. For successful tracking and camera control, the tracker must be fast enough, or has to be able to predict accurately the next position of the target. Therefore, standard benchmarks do not allow to assess properly the quality of a tracker for the PTZ scenario. In this work, we use a virtual PTZ framework to evaluate different tracking algorithms and compare their performances. We also extend the framework to add target position prediction for the next frame, accounting for camera motion and processing delays. By doing this, we can assess if predicting can make long-term tracking more robust as it may help slower algorithms for keeping the target in the field of view of the camera. Results confirm that both speed and robustness are required for tracking under the PTZ scenario.