An Object Detection and Classification Method using Radar and Camera Data Fusion

Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.