Title: Nearing or Surpassing: Overall Evaluation of Human-Machine Dynamic Vision Ability. Abstract: Dynamic visual ability (DVA), a fundamental function of the human visual system, has been successfully modeled by many computer vision tasks in recent decades. However, the prosperity developments mainly concentrate on using deep neural networks (DNN) to simulate the human DVA system, but evaluation systems still simply compare performance between machines, making it tough to determine how far the gap is between humans and machines in dynamic vision tasks. In fact, neglecting this issue not only makes it hard to determine the correctness of current research routes, but also cannot truly measure the DVA intelligence of machines. To answer the question, this work designs a comprehensive evaluation system based on the 3E paradigm -- we carefully pick 87 videos from various dimensions to construct the environment, confirming it can cover both perceptual and cognitive components of DVA; select 20 representative machines and 15 human subjects to form the task executors, ensuring that different model structures can help us observe the effectiveness of research development; and finally quantify their DVA with a strict evaluation process. Based on detailed experimental analyses, we first determine that the current algorithm research route has effectively shortened the gap. Besides, we further summarize the weaknesses of different executors, and design a human-machine cooperation mechanism with superhuman performance. In summary, the contributions include: (1) Quantifying the DVA of humans and machines, (2) proposing a new view to evaluate DVA intelligence based on the human-machine comparison, and (3) providing a possibility of human-machine cooperation. The datasets, toolkits, codes, and evaluation metrics will be open-sourced to help researchers develop intelligent research on dynamic vision tasks.