针对嵌入式平台往往算力受限的应用背景, 提出了一种低时间复杂度的、适用于复杂场景的目标跟踪算法——CTSTC算法。算法由自适应更新的时空上下文目标跟踪环节和自适应更新的压缩感知目标辅助定位环节两部分构成, 当时空上下文跟踪结果不可靠时, 启动压缩感知目标辅助定位环节, 如果辅助定位后的结果可靠, 则采用辅助定位结果校正时空上下文跟踪环节。算法运行速度与时空上下文算法(STC)接近, I5CPU下测试可达每秒1 577帧, 远高于其他常用算法, 是一种运算速度极高的目标跟踪算法, 但算法在复杂环境下的鲁棒性却有所提升。使用OTB2013数据集进行测试, 较STC算法, CTSTC精度提升12.8%, 成功率提升27.5%。算法在以DM6437为核心的小型目标跟踪系统上进行测试, 可以实现实时稳定跟踪。

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.