Chen, Jianhui and Le, Hoang M. and Carr, Peter and Yue, Yisong and Little, James J. (2016) Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees. In: IEEE Conference on Computer Vision and Pattern Recognition. . (In Press) http://resolver.caltech.edu/CaltechAUTHORS:20160628-154742818
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We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach.
|Item Type:||Book Section|
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|Deposited By:||Joy Painter|
|Deposited On:||28 Jun 2016 23:01|
|Last Modified:||28 Jun 2016 23:02|
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