Multi-object tracking is an active computer vision problem that has gained consistent interest due to its wide range of applications in many areas like surveillance, autonomous driving, entertainment, and, gaming to name a few. In the age of deep learning, many computer vision tasks have benefited from the convolutions neural network. They have been optimized with rapid development, whereas multi-target tracking remains challenging. A variety of models have benefited from the representational power of deep learning to tackle this issue. This paper inspects three CNN-based models that have achieved state-of-the-art performance in addressing this problem. All three models follow a different paradigm and provide a key inside of the development of the field. We examined the models and conducted experiments on the three models using the benchmark dataset. The quantitative results from the state-of-the-art models are listed in the standard metrics and provide the basis for future research in the field.