A vehicle retrieval and recognition system based on smart phones

Traditional vehicle recognition and retrieval systems are almost based on vehicle license plate recognition, which requires the user to provide images containing the license plate of the vehicle. In order to recognize and retrieve vehicles more convenient and efficient, and solve the problem of missing or wrong license plate, this paper proposes a method based on the overall appearance characteristics of the vehicles. The proposed system using a smart phone camera to collect vehicle images and uploads the images to the server through 4G network. The server performs vehicle retrieval and recognition and sends the results back to the phone. To reduce the amount of calculation and improve the accuracy of system, saliency areas of training images are extracted, A hessian-affine detector is used to detect the features of the input image and the SIFT descriptor transforms each feature into a 128-dimensional vector which is finally quantized as "visual word", the ACS (Adjusted Cosine Similarity) and SVM methods are also adopted. The experiments show that the precision of the vehicle recognition is above 96%, and this system has good real-time performance.