Multi-object tracking using correlation filters in video analytics applications

Abstract

In various examples, correlation filters may be learned for object tracking based at least on occlusion maps. When learning a correlation filter from an image area, an occlusion map may be applied to the image area that masks, excludes, and/or blurs occlusions of the target object. The correlation filter may be learned from a modified image, thereby reducing or eliminating learning from occlusions while still allowing for learning the target object from exposed portions. The occlusion maps may be generated using a machine learning model, such as a Gaussian Mixture Model (GMM) that is trained (e.g., using the image areas used to learn the correlation filter) using the target object as a background so that occlusions are detected as foreground.