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Because of the technical and cost constraints on traditional measurement methods, there is a lack of long-term driving behavior data from natural traffic scenes, and this situation has been hindering research progress into driving behavior modeling and other related topics. Thanks to high-definition cameras and advanced visual measurement methods, traffic visual detection is entering a new stage of traffic visual measurement, and thus we can expect to achieve accurate segmentation, positioning, and measurement for road vehicles from live video to meet the requirement for field test data in behavior modeling. To measure driving behaviors in a cost-effective manner, the authors propose a comprehensive visual measurement approach that could perform well in complex traffic scenes. Specifically, they describe a procedure for traffic visual measurement, some preliminary algorithms, and some representative experimental results. Comparisons between the proposed method and three traditional ones (driving simulator, in-vehicle data recorder, and remote-sensing camera) indicate that the biggest advantage of the proposed method is it can measure driving behaviors from live video. Hence, the ongoing research will greatly benefit cognition in driving behavior models.