To solve the issue of complicated structure and low real-time performance of the two-step multiple object tracking by detection algorithm, a real-time multiple object tracking algorithm founded on modified YOLOv4-Tiny and DeepSORT algorithm is proposed. The depthwise separable convolution is employed in YOLOv4-Tiny, to reduce the calculation of the model; The detection branches are increased to 3, and multi-scale feature fusion structure is built to decrease the missed ratio of tiny objects; The modified GC attention module is used to extract the global context features of the model. In the tracking part, the pedestrian motion model of DeepSORT is optimized and the appearance model is reconstructed, the detection and tracking algorithms are combined and experimented in MOT16 test sequences finally. The results show that the total parameters of the improved algorithm are 4.2M, 52% less than the original algorithm and 5.2% more MOTA, faster processing speed under GPU, and the tracking speed of an average of 11 frames per second can be achieved under a single CPU, which can fulfill the requests of precision and speed for multiple object tracking mission in low-calculation devices.