A major concern for interconnected power grid systems is low-frequency oscillation, which limits the scalability and transmission capacity of power systems. Undamped or poorly damped oscillations will lead to undesirable conditions or even a catastrophic system blackout. Real-time synchrophasor data can be used to reliably detect and control these low-frequency oscillations in order to mitigate their catastrophic impact. In this paper, two low complexity tracking algorithms are proposed to identify and monitor low-frequency oscillations; namely, a fast subspace tracking algorithm and a gradient descent based fast recursive algorithm. Initially, both methods perform a one-time matrix pencil method on the power spectrum matrix of real-time Phasor Measurement Unit (PMU) data to detect low-frequency oscillations. This is then followed by two different low-complexity algorithms to fast track the low-frequency oscillations. While the first method uses a recursive fast data projection method-based algorithm, the latter runs a gradient-descent based fast recursive algorithm on every PMU to track and monitor low-frequency oscillations. Both methods have been compared to other state-of-the-art techniques, such as matrix pencil method, frequency domain decomposition, and TLS-ESPRIT. We have shown that the proposed approaches are capable of achieving performance with high accuracy, especially in terms of computational complexity for a large system with many PMUs.