To build the integrated intelligent traffic monitoring system based on the cooperation of human, road, vehicle and cloud, the research of multi-object tracking has wide application potentials. Traditional methods with handcrafted features are hard to fully represent high-level information, making it difficult to track multi-targets in complex scenes. Deep learning with its powerful learning ability, has gradually been used in various industries and fields, setting off a wave of smart technologies. To understand the research progress on the multi-object tracking algorithms based on deep learning, firstly, the pros and cons of three tracking algorithms, namely tracking by detection, joint detection and tracking as well as multi-object tracking with single object tracker, are compared. Then, the applications of multi-object tracking algorithm in intelligent traffic monitoring systems are introduced. Finally, the problems and challenges of multi-object tracking algorithm are concluded, and the growing trend of multi-object algorithms in the intelligent transportation field is discussed and forecasted.