Low time complexity target tracking algorithms based on embedded platform

Aimed at the application background of embedded platform which is often limited in computing power, this paper proposed a low time complexity target tracking algorithm, CTSTC algorithm, which was suitable for complex scenes. The algorithm consisted of two parts:the main part constituted by the spatio-temporal context target tracking based on adaptive update of model and the aided target location part constituted by compressive tracking based on adaptive update of model. When the results of spatio-temporal context tracking were unreliable, the aided location part was activated. If the results of aided location were reliable, the aided location result was used to correct the spatio-temporal context tracking part. The running speed of the algorithm was close to that of the spatio-temporal context learning algorithm (STC). The test on I5CPU can reach 1 577 frames per second, which was much faster than other commonly used algorithms. It was a very fast target tracking algorithm, but the robustness of the algorithm in complex environments was improved. Using OTB2013 data set to test, compared with STC algorithm, CTSTC accuracy increased by 12.8%, success rate increased by 27.5%. The algorithm is tested on a small target tracking system with DM6437 as the core, which can achieve real-time stable tracking.