This study researches on the visual detection and following of object people by indoor monocular robots，which includes scene change detection algorithm, visual object people detection algorithm, visual object tracking algorithm, and robot following, with focuses on scene change detection algorithm and visual object tracking algorithm. A high speed scene change detection algorithm judges whether the scene changes by constructing scene models. If the scene changes, the algorithm outputs the change region, which is used by the visual object detection algorithm. The experiment shows this algorithm speeds up the system and alleviates the latency of robots. The visual object tracking algorithm combines the appearance model and map information obtained in SLAM process. The map information can judge which part of object bounding box is actually the background, which can reduce the effect of occlusion and object scale change on appearance model. This algorithm improves visual object tracking performance in the experiments. This paper applies the latest deep neural networks to do visual object people detection. We train a small deep neural network with enhancement on indoor people, which achieves a good balance between running speed and detection performance. Based on the visual detection and visual tracking of target, we accomplish robot following. Since monocular robots can only get the bearing information of target, the goal of robot following is keeping the target in the horizontally middle point of image plane. The robot can successfully follow human even if the person is partially occluded.