1. A method for object classification by an electronic device, comprising: obtaining an image frame that includes an object; determining samples from the image frame, wherein each of the samples represents a multidimensional feature vector, wherein the samples comprise non-support vector negative samples, non-support vector positive samples, and support vector samples; adding the samples to a training set for the image frame; pruning one or more samples from the training set to produce a pruned training set, wherein one or more of the non-support vector negative samples are pruned first, wherein one or more of the non-support vector positive samples are pruned second based on a comparison between a number of samples remaining after pruning the one or more non-support vector negative samples and a sample number threshold, and wherein one or more of the support vector samples are pruned third if necessary to avoid exceeding the sample number threshold; and updating classifier model weights based on the pruned training set.