Traffic Pattern Recognition Algorithm Based on Multi-Scale Feature Extraction

The characteristics of various traffic modes are analyzed, the fuzzy neural network is used to identify the traffic modes, the eigenvalues of the identified traffic modes are determined, and the correctness of the network is tested by traffic flow. The manual approaches significantly capture the spatial and temporal relationships as well as enhance the model complexity through examining both connected and unconnected roads. In feature extraction process, initially the input image is provided to the computation gradients process and processed output provided to the collect HOG over image and it provides the HOG feature. The test results show that the application of fuzzy neural network to traffic pattern recognition can accurately reflect the situation of traffic flow. Taking the results of traffic pattern recognition as the basis of road patency can guide the group control system to adopt corresponding strategies according to different traffic conditions.