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Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees

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)

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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.

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Record Number:CaltechAUTHORS:20160628-154742818
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:68721
Deposited By: Joy Painter
Deposited On:28 Jun 2016 23:01
Last Modified:28 Jun 2016 23:02

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