Interactions between dendritic cells (DCs) and circulating tumor cells (CTCs) have attracted wide attention in tumor immunity research, especially on specifically targeted tumors. However, feature extraction and noninvasive tracking of DCs and CTCs are challenges that have long existed in biomedicine. In this study, we developed an automatic algorithm for identifying, counting, tracking, and segmenting fluorescently-labeled CTCs and DCs from the blood vessels of mouse ears. For fluorescence imaging, we constructed an in vivo image flow cytometry system to capture dual-channel (green and red) fluorescence image sequence simultaneously. To achieve real-time functions for the CTCs and DCs, we developed a motion detection method based on codebook which first performs background modeling and then cone-shaped area search procedures for postprocessing. We validated this novel algorithm through in vivo image sequencing, through which we observed the interaction of CTCs and DCs. Moreover, we used quantitative colocalization to determine the relationship between CTCs and DCs. The quantitative results illustrated the interactions between CTCs and DCs, as did image sequences which are promising for driving research on cancer immunotherapy in the future.