As one of the common daily behaviors, walking could reveal much important information, such as one's identity and health condition. Fine-grained walking information such as walking velocity, walking direction, the number of steps, and stride length could provide important references for indoor tracking, gait analysis, elder care, and other context-aware situation applications. Thus, the perception of human walking utilizing the environmental Wi-Fi signal has been widely concerned by researchers in recent years. In order to achieve the perception of human walking, current methods usually need to gather a lot of walking data and then extract signal feature from extensive data through empirical observation or off-line training. However, due to the lack of theoretical instruction, the extracted signal feature is indirect and often contains redundant information of environment and sensing target. Therefore, as long as there is a change of the environment or sensing target, these systems have to regather data and relearn the signal feature for new situation. This would cause difficulties when applied in real life with varied wireless environment. Different from these works, this study has achieved the walking recognization in daily continuous activities without any learning requirement. Moreover, the fine-grained parameters such as walking velocity, walking direction, the number of steps, and stride length have been estimated in order to provide crucial context for upper layer context-aware applications. Specially, by analyzing the relationship between channel state information (CSI) and Doppler effect introduced by human movement, a Doppler velocity model is firstly established revealing that the theoretical relationship between human movement and CSI variation. Then by utilizing the MUSIC algorithm, the Doppler velocity could be obtained from Wi-Fi CSI which serves as an effective signal feature in revealing human movement and unrelated to the environment and human target. Finally, by studying the relationship between Doppler velocity and real human walking velocity, walking behavior as well as estimating fine-grained walking parameters could be recognized. Through extensive experiments done by different volunteers in different environments, the results have demonstrated the accuracy and robustness of the system. The system achieves an accuracy of 95.5% in walking recognition, a relative median error of 12.2% in walking velocity estimation, a median error of 9° in walking direction estimation, an accuracy of 90% in step counting and a median error of 0.12m in stride length estimation.