Multiple people tracking is a significant sub-problem of object tracking with high demand during recent years. In the large view scene, the main difficulties are that the objects are small and they may be occluded or have sudden appearance changes. So most existing methods have high ID switches (a evaluation metric for multiple people tracking) in large view scene. We propose a multiple people tracking method that focus on solving high ID switches in large view scene. Our method uses intersection over union (IOU) information that is not sensitive to appearance changes and Euclidean distance-based appearance similarity that is helpful in solving the problem of occlusions to associate data. In order to make our Euclidean distance-based appearance similarity metric work better, we employ a soft-margin loss function to train a convolutional neural network (CNN), it can make the features extracted by the CNN more suitable for our similarity metric, so our method can effectively solve high ID switches problem. IOU-based data association has low computational complexity and the CNN is a lightweight network, it makes our method have real-time speed. On the other hand, we propose a multiple people tracking dataset of large view scene for research. We design our dataset according to the standards of MOT Challenge benchmark and we select yolov3 detector that has relatively good performance for small objects as a public detector. Finally, our method is compared with several multiple people tracking methods on our dataset. The experimental results show that our method has a better performance in large view scene.