Video analytics accuracy using transfer learning

Abstract

Systems and methods are provided for increasing accuracy of video analytics tasks in real-time by acquiring a video, and identifying fluctuations in the accuracy of video analytics applications across consecutive frames of the video. The systems and methods can support decision-making based on results of video analytics tasks. The fluctuations are quantified based on an average relative difference of true-positive detection counts across consecutive frames. Fluctuations in accuracy are reduced by applying transfer learning to a deep learning model trained using images, and retraining the deep learning model using video frames. A quality of object detections is determined based on an amount of track-ids assigned by a tracker across different video frames. Optimization of the reduction of fluctuations includes iteratively repeating the identifying, the quantifying, the reducing, and the determining the quality of object detections until a threshold is reached. Frame predictions are generated using the retrained deep learning model.