Spontaneous generation, natural dissipation, split, and merger of convective cells within the life cycle of the mesoscale convective system (MCS) is the main problem of most existing tracking algorithms. To address this issue, an algorithm called spatio-temporal context and extended maxima transform (SCEMT) is proposed for tracking convective cells using satellite infrared image sequences. In order to track convective cells, the presented method uses the extended maxima transform technique to detect convective cells, learning a spatio-temporal context model, updating scale and variance, and calculating the confidence map of adjacent moments. Case studies demonstrate the effectiveness of the proposed method in different phases of MCS life cycle. The SCEMT method is evaluated utilising contingency table approach on FY-2F data sets. This novel method has a good discrimination skill (probability of detection 90%, false alarm ratio 7%, and critical success index 84%), and provides a correct tracking of convection motion.