The industry of UAVs has experienced a boost in recent years, and developments on both the hardware and algorithmic side have enabled smaller and more accessible drones with increased functionality. This thesis investigates the possibilities of autonomous target recognition and tracking in small, low-cost drones that are commercially available today. The design and deployment of an object recognition and tracking algorithm on a Crazyflie 2.1, a palm-sized quadcopter with a weight of a few tens of grams, is presented. The hardware is extended with an expansion board called the AI-deck featuring a fixed, front-facing camera and a GAP8 processor for machine learning inference. The aim is to create a vision-based autonomous control system for target recognition and following, with all computations being executed onboard and without any dependence on external input. A MobileNet-SSD object detector trained for detecting human bodies is used for detecting a person in images from the onboard camera. Proportional controllers are implemented for motion control of the Crazyflie, that process the output from the detection algorithm to move the drone to the desired position. The final implementation is tested indoors and proved to be able to detect a target and follow simple movements of a human moving in front of the drone. However, the reliability and speed of the detection need to be improved to achieve a satisfactory result.