Accurate traffic data collection is crucial to the relevant authorities in ensuring the planning, design, and management of the road network can be done appropriately. Traditionally, traffic data collection was done manually by having human observers at the site to count the vehicle as it passes the observation point. This approach is cost-effective; however, the accuracy can’t be verified and may cause danger to the observers. Another common approach is utilizing sensors that need to be installed underneath the road surface to collect traffic data. The accuracy of the data reading from the sensor is highly dependent on the sensor installation, calibration, and reliability which usually deteriorated over time. For these reasons, vision-based approaches have become more popular in traffic flow estimation tasks. Nevertheless, conventional image processing techniques which utilize background subtraction-based approach may face problems in complex highway environment where the number of the vehicle is high, a large gap in vehicle sizes of different classes and high occlusion rate. Thus, in this paper, a real-time vehicle counting in a complex scene for traffic flow estimation using a multi-level convolutional neural network is proposed. By exploiting the capabilities of deep-learning models in delineating and classifying objects in an image, it is shown that the system can achieve average counting accuracy of 97.53% and a weighted average of counting with classification accuracy of 91.5% validated on 585 minutes of highway videos collected from four different cameras; viewing at different vehicle's angles. The system is also capable of running in real-time.