Helper robots are widely used in various situations, for example, at airports and railway stations. This paper presents a pipeline to multiplex the tracking and detection of a person in dynamic environments using a stereo camera in real-time. Recent developments in object detection using ConvNets have led to robust person detection. These deep convolutional neural networks generally fail to run with high frame rates on devices with less computing power. Trackers are also used to retain the identity of the target person as well as impose fewer constraints on hardware. A concept of multiplexed detection and tracking is used, which makes the pipeline faster by many folds. TurtleBot-2 is used for prototyping the robot and tuning of the motion controller. Robot Operating System (ROS) is used to set up communication between various nodes of the pipeline. The results found were comparable to current state-of-the-art person followers and can be readily used in day to day life.