自适应实时压缩感知跟踪算法

【目的】解决实时压缩感知跟踪算法分类器无法适应目标外观变化及过更新的问题。【方法】根据当前跟踪结果目标模型的哈希指纹与上一帧目标模型的哈希指纹之间的汉明距离(Hamming distance),在线实时调整分类器,以提高实时压缩感知目标跟踪算法的自适应能力。【结果】自适应实时压缩感知跟踪算法的跟踪成功率比实时压缩感知跟踪算法提高13%,在目标大小为40pixel×43pixel时,跟踪速率为37fps,满足实时性要求。【结论】本研究建立的方法在背景中存在与目标有一定相似性的物体,且目标姿态、纹理变化和光照变化较大等情况下,能快速获取跟踪目标,并且具有较强的鲁棒性和准确性。

Adaptive Real-time Compressive Sensing Tracking Algorithm

Objective]To solve the problem of real-time compressed sensing tracking algo-rithm,which is the inadaptability of classifier to the changes of the target appearance and the over update.Methods]Base on Hamming distance between the Hash fingerprints of current target and original one,classifier is adjusted in real time,which improved the adaptive ca-pacity of the real-time compressed sensing tracking.Results]As compared with the real-time compressive sensing tracking algorithm,the proposed algorithm improves the success rate by 13%,and average computing frame rate is 37 frames when the target scale is 40 pixel×43 pixel,which satisfies the requirements of real-time tracking.Conclusion]The proposed algo-rithm is tested with variant video sequences.The results show that the proposed algorithm is capable of speedily and accurately capturing the tracking target by target gestures,textures, or significant light change.