The task of person re-identification is to match pedestrian images observed from different cameras in a non-overlapping multi-camera surveillance systems. In this article, a video-based person re-identification method is proposed. HOG3D is extracted as temporal and spatial feature and the DenseNet model pre-trained on MSCOCO is adopted to fine-tune the parameters for person re-identification, and the fine-tuned model is used to extract the feature from person image. The two features are combined to describe the person video clip.Finally the metric learning model is applied to measure distance between person pairs. We evaluate our approach by operating in-depth experiments in two video-based benchmarks, and the experimental results on the two benchmark show significant and consistent improvements over the state-of-the-art methods.