To solve the target tracking problem in computer vision, this study proposes a strategy based on a convolutional neural network (CNN) that extracts depth features and adaptively blends with edge features to realize the tracking algorithm for video targets. The low-level network of CNN can acquire a part of the spatial structure and shape of the target. High-level network of CNN can obtain relatively abstract partial semantic information. Herein, depth features are extracted by the second convolutional layer Conv1-2, the fourth convolutional layer Conv2-2, and the last convolutional layer Conv5-3 in VGG16 neural network. The above mentioned features are fused with the edge feature adaptively to achieve video object tracking. Herein, the experimental verification and analysis of the proposed method are conducted on the OTB100 dataset. Results show that the proposed method can achieve accurate positioning of the target.