1. A method for detecting an object, comprising: acquiring a first video of a source domain where a vision-based object detector is trained; performing change detection on the first video to collect one or more first localized, unlabeled sample images from the source domain; acquiring a second video of a target domain where the vision-based object detector is used; performing change detection on the second video to collect one or more second localized, unlabeled sample images from the target domain; determining a domain adaptation parameter based on one or more domain shift properties between the one or more first localized, unlabeled sample images from the source domain and the one or more second localized, unlabeled sample images from the target domain, wherein determining the domain adaptation parameter comprises converting the one or more first localized, unlabeled sample images from the source domain into corresponding histogram oriented gradient features; and adapting the vision-based object detector to be used for object detection in the target domain based on the domain adaptation parameter, wherein adapting the vision-based object detector comprises using a linear support vector machine as a classifier for the source domain and the target domain, wherein W′=WG and b′=W (μ S −Gμ T ), and wherein: W′ represents a linear weighting vector for the target domain, W represents a linear weighting vector for the source domain, G represents a domain shift matrix, b′ represents a bias for the target domain, μ S represents a sample means of a data set of the histogram oriented gradient features of the one or more first localized, unlabeled sample images from the source domain, and μ T represents a sample means of a data set of the histogram oriented gradient features of the one or more second localized, unlabeled sample images from the target domain.