US 12,348,859 B2
Adaptive perceptual quality based camera tuning using reinforcement learning
Kunal Rao, Monroe, NJ (US); Sibendu Paul, West Lafayette, IN (US); Giuseppe Coviello, Robbinsville, NJ (US); Murugan Sankaradas, Dayton, NJ (US); Oliver Po, San Jose, CA (US); and Srimat Chakradhar, Manalapan, NJ (US)
Assigned to NEC Corporation, Tokyo (JP)
Filed by NEC Laboratories America, Inc., Princeton, NJ (US)
Filed on Sep. 13, 2023, as Appl. No. 18/466,296.
Claims priority of provisional application 63/406,709, filed on Sep. 14, 2022.
Prior Publication US 2024/0089592 A1, Mar. 14, 2024
Int. Cl. H04N 23/60 (2023.01); H04N 23/61 (2023.01)
CPC H04N 23/64 (2023.01) [H04N 23/61 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method for dynamically tuning camera parameters in a video analytics system (VAS) to optimize analytics accuracy, comprising:
capturing a current scene using a video-capturing camera;
learning optimal camera parameter settings for the current scene using a Reinforcement Learning (RL) engine by defining a state within the RL engine as a tuple of a first vector representing current camera parameter values and a second vector representing measured values of captured frames of the current scene, and defining sets of actions for modifying parameter values and maintaining the current parameter values;
estimating a quality of the captured frames using a perceptual no-reference quality estimator, and tuning the camera parameter settings based on the quality estimator and the RL engine to optimize analytics accuracy of the VAS;
evaluating an effectiveness of the tuning by perceptual Image Quality Assessment (IQA) to quantify a quality measure;
iteratively adaptively tuning the camera parameter settings in real-time using the RL engine, responsive to changes in the scene, based on the learned optimal camera parameter settings, the state, the quality measure, and the set of actions, to further optimize the analytics accuracy until a threshold condition is reached.