Application of deep classification networks in classification of typical targets in road traffic is investigated in this paper. Deep classification networks are constructed by combining such target representation methods as original gray level image, HOG feature histogram, Canny edge image and eigen features with Deep Belief Networks(DBN), to realize the classification function for four typical targets in road traffic: pedestrian, biker, vehicles and others in the real scene. In order to assist in training of DBN based deep people/vehicle classification networks, an image database of typical road targets called NUPTERC is established, with rules and methods for its establishment. And then experiments are constructed with NUPTERC image database, to test the proposed deep classification networks, and a comparison is made with other classification methods for people and vehicles. It is proven that the deep classification networks can achieve satisfactory classification accuracy under the condition of meeting the real time performance. Finally, people/vehicles classification algorithm based on DBN5Canny is applied to the “cloud platform for intelligent video analysis” developed by our center, realizing functions of real time accurate analysis and classification of typical targets in road traffic.