In order to meet the long-term tracking requirements of platforms with weak computing power, such as the embedded system with C64x+ DSP as the computing core, a long-term tracking algorithm with low time complexity was proposed, which consisted of two parts. One part was the continuous tracking part and the other part was the target detection part. The continuous tracking part was based on adaptive update spatio-temporal context algorithm(STC), and the target detection part used normalized cross-correlation matching algorithm. If there were no special tracking scenarios such as field of view and fast moving target, the continuous tracking part outputted tracking results. After tracking failure, the whole image was processed in the target detection part, and the target was re-locked as long as the target appeared in the image again. Experiments show that target detection can accurately detect the target after it appeared, which meets the requirements of long-term tracking. At the same time, the robust of continuous tracking is also improved because the target location is redefined by the target detection part, when the result is not reliable. Using OTB2013 data set to test, the accuracy of this algorithm is 4.95% higher than that of STC algorithm.