The accurate detection of people in indoor environments requires high-cost devices, while low-cost devices, in addition to low accuracy, offer little information about the monitored events. The perturbations that result from indoor movements affect the signals received by 802.11 interfaces. Hence, an 802.11 device becomes a widely available, low-cost, and reasonably accurate solution for several applications. This paper presents WiDMove, a proposed technique to detect the entry and exit of persons, within an indoor environment, using the channel state information (CSI) measurements, which is provided by the IEEE 802.11n compliant devices. Based on the gathered CSI measurements, we utilized frequency-time analysis methodology to build an efficient features vector based on Short-Time Fourier Transform (STFT) and Principal Component Analysis (PCA). We used the extracted features to train and develop a Support Vector Machine (SVM) classifier, which provided very promising initial results. Our initial results have an accuracy near 80 %.