Methods and systems are provided for classifying an object appearing in multiple sequential images. The process includes determining a neural network classifier having multiple object classes for classifying objects in images; determining a likelihood classifier model comprising a likelihood vector of class probability vectors; for each image z, running the image multiple respective times through the neural network classifier, applying dropout each time, to generate a point cloud of class probability vector values {ϒ t }; calculating a vector of posterior distributions {λ t } for each class and for each of the multiple {ϒ t }, where calculating each class element of {λ t } includes calculating a product of the respective element of the class probability vectors and an element of the posterior distribution of a prior image; randomly selecting a subset of {λ t } to form a new subset of {λ t }; and repeating the calculation of the subset {λ t } for each of the images, to determine a cloud of posterior probability vectors approximating a distribution over posterior class probabilities, given all the multiple sequential images.
SYSTEM AND METHOD FOR SEQUENTIAL PROBABILISTIC OBJECT CLASSIFICATION