由于现有目标跟踪算法在复杂环境下易发生目标漂移甚至跟踪丢失，故本文提出了以双重采样粒子滤波为框架，基于增量深度学习的目标跟踪算法。该算法在粒子滤波中引入粒子集规模自适应调整的双重采样来解决粒子衰减及贫化问题，并利用无监督特征学习预训练深度去噪自编码器以克服跟踪中训练样本的不足。将深度去噪自编码器应用到在线跟踪中，使提取的特征集合能够有效表达粒子图像区域。在深度去噪自编码器中添加了增量特征学习方法，得到了更有效的特征集以适应跟踪过程中目标外观变化。该方法还用线性支持向量机对特征集合进行分类，提高对粒子集合的分类精度，以得到更精确的目标位置。在复杂环境下对不同图片序列进行的实验表明: 该算法的跟踪综合评价指标为94%、重叠率为74%，平均帧率为13 frame/s。与现有的跟踪算法相比，本算法有效地解决目标漂移甚至跟踪丢失问题，并且对遮挡、相似背景、光照变化、外观变化具有更好的鲁棒性及精确度。

As current tracking algorithms lead to target drift or target loss in the complex environment, a tracking algorithm based on the incremental deep learning was proposed under a double-resampling particle filter framework. To solve the problem of particle degradation and depletion, the double-resampling method was introduced to adapt to the particle size in particle filtering and a Stacked Denoising Autoencoder(SDAE) was pre-trained by the unsupervised feature learning to alleviate the lack of training samples in visual tracking. Then, the SDAE was applied to online tracking, so that the extracted feature sets could express the region image representations of the particles effectively. The incremental feature learning was introduced to the encoder of SDAE, the feature sets were optimized by adding new features and merging the similar features to adapt to appearance changes of the moving object. Moreover, a support vector machine was used to classify the features then to improve the classification accuracy of the particles and to obtain a higher tracking precision. According to the results of experiments on variant challenging image sequences in the complex environment, the F-measure and the overlapping ratio of the presented algorithm are 94%, 74%, respectively and the average frame rate is 13 frame/s. Compared with the state-of-the-art tracking algorithms, the proposed method solves the problems of target drift and target loss efficiently and has better robust and higher accuracy, especially for the target in the occlusions, background clutter, illumination changes and appearance changes.