Multiple live videos are streamed to the security room in the video surveillance system of a museum, shopping mall, plaza, etc. For public safety and crowd research, multi-person tracking is essential in these small-scale distributed multi-camera systems. This paper presents a multi-person multi-camera tracking framework for live stream videos. It is compatible with both overlapping and non-overlapping views. Since the tracking framework cannot consistently achieve real-time performance, live stream videos are probably sub-sampled. This sub-sampled issue will enlarge the error of every tracked person’s predicted bounding box at each frame. A time-based motion model studying the precise time intervals among sub-sampled frames is proposed. Besides, cameras have different orientations and exposures, resulting in false matches when tracked pedestrians cross the camera boundaries. When a tracked person is out of a camera’s view, its motion model will fail, and its tracking identity will probably switch when reentering the same camera. An improved multi-person matching cascade scheme is proposed to solve these problems. It can increase the accuracy of inter-camera person re-identification (Re-ID) by taking advantage of association priorities. Experiments are implemented with the EPFL dataset and live stream videos. Results show that the proposed method has robust performances in different situations.