Autonomous Camera Trajectory Prediction via Temporal Pattern Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous camera systems struggle to produce smooth and dynamic video recordings of events like sporting competitions, as they lack the ability to anticipate actions and frame shots with sufficient 'lead room', resulting in robotic-looking videos and jittery camera movements due to inadequate handling of temporal patterns and context in traditional smoothing methods.
Innovation Solution
The implementation of a temporally consistent search and learn (TC-SEARN) algorithm for training a regressor that learns smooth spatiotemporal patterns, allowing autonomous cameras to predict camera trajectories that mimic human operators by balancing reconstruction error against temporal smoothness, using feature vectors and operational data from human-operated cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If traditional smoothing methods are used to filter camera movement data, then camera movements become smoother, but the system loses temporal patterns and context, causing inability to track fast moving objects
Solution Approach 1:
The system performs preliminary smoothing of noisy tracking data before feeding it to the machine learning model. This preprocessing step removes high-frequency noise while preserving the underlying temporal patterns, allowing the model to learn from cleaner data without losing critical temporal context
Solution Approach 2:
The system uses a recurrent neural network architecture that incorporates feedback loops to maintain temporal context. The model processes sequences of smoothed data points and uses hidden states to retain information about previous frames, enabling it to capture temporal patterns while working with smoothed input data
2Stability of the object's composition
If heavy filtering is applied to smooth camera signals, then camera movements become smoother, but the camera cannot change position quickly enough to track fast moving objects
Solution Approach 1:
The system employs a dynamic filtering approach where the smoothing parameters are not fixed but adapt based on the content being tracked. The filter strength varies over time, allowing aggressive smoothing during stable periods and reduced smoothing when rapid movement is detected, thus maintaining both smoothness and responsiveness
Solution Approach 2:
The machine learning model learns optimal smoothing parameters from training data and dynamically adjusts filter characteristics based on the current scene context. This allows the system to change filtering behavior adaptively, maintaining smooth camera movements while preserving the ability to respond quickly to important events
3Ease of operation
If hand-coded equations are used for camera planning, then the system can determine camera positions, but the output videos look robotic and lack sufficient lead room for dynamic activities
Solution Approach 1:
The system replaces hand-coded mechanical camera control equations with a machine learning-based camera planning system. The neural network learns optimal camera positioning strategies from training data, enabling it to anticipate actions and position cameras more naturally than rigid mathematical formulas, thus improving video quality and reducing robotic appearance
Data Source
AI summary
The disclosure provides an approach for predicting trajectories for real-time capture of video and object tracking, while adhering to smoothness constraints so that predictions are not excessively jittery. In one embodiment, a temporally consistent search and learn (TC-SEARN) algorithm is applied to train a regressor for camera planning. A automatic broadcasting application first receives video input captured by a human-operated camera and another video input captured by a stationary camera with a wide field of view. The automatic broadcasting application extracts feature vectors and pan-tilt-zoom states from the stationary camera input and human-operated camera input, respectively. The automatic broadcasting application further applies the TC-SEARN algorithm to learn a sequential regressor for predicting camera trajectories, based on the extracted feature vectors and pan-tilt-zoom states. The TC-SEARN algorithm itself is able to learn the regressor using a loss function which enables decision trees to reason about spatiotemporal smoothness via an autoregressive function.