Multiple object tracking in video by combining neural networks within a bayesian framework

摘要

Techniques for multiple object tracking in video are described in which the outputs of neural networks are combined within a Bayesian framework. A motion model is applied to a probability distribution representing the estimated current state of a target object being tracked to predict the state of the target object in the next frame. A state of an object can include one or more features, such as the location of the object in the frame, a velocity and/or acceleration of the object across frames, a classification of the object, etc. The prediction of the state of the target object in the next frame is adjusted by a score based on the combined outputs of neural networks that process the next frame.