In recent years, unmanned aerial vehicles (UAVs) have been widely used in ocean target tracking and image acquisition for processing. Due to the limited energy of the UAV and the high computational complexity associated with image processing tasks, a lightweight energy-saving target tracking scheme is designed for the UAV, and the unmanned surface vehicle (USV) based mobile edge computing (MEC) networks are adopted to share the computing load of the UAV. Due to the randomness of the environment, we formulate data processing, computation offloading, resource allocation, and target-tracking as a joint stochastic optimization problem. This paper investigates a two-stage optimization scheme to address the problem. First, we employ a Lyapunov-based approach to convert the stochastic optimization problem into a deterministic per-time slot problem under communication and computing resources constraints. Then, we develop a real-time target tracking scheme for the UAV based on the Elman neural network. Numerical results validate that the designed tracking scheme can effectively minimize propulsion energy consumption while maintaining a high success rate in tracking. Furthermore, the proposed method balances data-related energy consumption, image detection accuracy, and stability of the data storage queue.