In this paper, we present a new method for human object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. In particular, to support content-relevant advertising, we employ the discriminatively trained part based model to detect human objects in a video and then select the ads that are related to the detected human objects. For human clothing advertising, we design a deep Convolutional Neural Network (CNN) using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothes retrieval. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for human object-level video advertising.