The traditional Convolutional Neural Network (CNN) can only extract local features for human behaviors and actions, which leads to low recognition accuracy for similar behaviors. To resolve this problem, a two-stream Non-Local Residual Network (NL-ResNet) based behavior recognition method was proposed. First, the RGB (Red-Green-Blue) frame and the dense optical flow graph of the video were extracted, which were used as the inputs of spatial and temporal flow networks, respectively, and a pre-processing method combining corner cropping and multiple scales was used to perform data enhancement. Second, the residual blocks of the residual network were used to extract local appearance features and motion features of the video respectively, then the global information of the video was extracted by the non-local CNN module connected after the residual block, so as to achieve the crossover extraction of local and global features of the network. Finally, the two branch networks were classified more accurately by A-softmax loss function, and the recognition results after weighted fusion were output. The method makes full use of global and local features to improve the representation capability of the model. On UCF101 dataset, NL-ResNet achieves a recognition accuracy of 93.5%, which is 5.5 percentage points higher compared to the original two-stream network. Experimental results show that the proposed model can better extract behavior features, and effectively improve the behavior recognition accuracy.

Concerning the phenomenon that common parking service could not satisfy the increasing demand of the private vehicle owners, an intelligent parking guidance system based on ZigBee network and geomagnetic sensors was designed. Real-time vehicle position or related traffic information was collected by geomagnetic sensors around parking lots and updated to center sever via ZigBee network. On the other hand, out-door Liquid Crystal Display (LCD) screens controlled by center sever displayed information of available parking places. In this paper, guidance strategy was divided into 4 levels, which could provide clear and effective information to drivers. The experimental results prove that the distance detection accuracy of geomagnetic sensors was within 0.4m, and the lowest loss packet rate of the wireless network in the range of 150m is 0%. This system can possibly provide solution for better parking service in intelligent cities.