Ground target tracking by unmanned aerial vehicle (UAV) is more difficult than the traditional video object tracking because the targets have often smaller size and are more blurred. A tracking algorithm is proposed based on l1 graph-based semi-supervised co-training. Firstly,color and texture features of labeled and unlabeled samples are extracted to construct two sufficient and redundant views. Secondly,l1 graph-based semi-supervised learning is adopted to train two separate classifiers in the co-training framework to replace the traditional supervised learning,which can improve the classifying accuracy under limited labeled training samples. Then,the classifiers from different views teach each other by co-training algorithm based on negative learning. Finally,the confidence distribution entropy of each view from two classifiers is evaluated as its own weight to achieve the tracking. Experimental results show that the proposed algorithm can effectively improve the discriminative ability of the classifier and achieve good tracking performance.