Deep learning-based trackers can always achieve high tracking precision and strong adaptability in diff-erent scenarios. However

because the number of the parameter is large and finetuning is challenging

the time complexity is high. To improve efficiency

we proposed a tracker based on fast deep learning through construction of a new network with less redundancy. The feature extractor plays the most important role in a visual tracking system. Based on the theory of deep learning

we proposed a deep neural network to describe essential features of images. Fast deep learning can be achieved by restricting the network size. With the help of GPU(graphics processing unit)

the time complexity of the network training is released to a large extent. Under the framework of particle filter

the proposed method combined the deep learning extractor with a support vector machine scoring professor to distinguish the target from the background. The condensed network structure reduced the complexity of the model. Compared with other deep learning-based trackers

the proposed method can achieve higher efficiency. The frame rate is kept at 22 frames per second on average. Experiments on an open tracking benchmark demonstrate that both the robustness and timeliness of the proposed tracker is promising when the appearance of the target changes contains translation

rotation

and scale

or the interference contains illumination

occlusion

and cluttered background. Unfortunately

the tracker is not robust enough when the target moves fast or the motion blur and some similar objects exist.