Transferability Study of Video Tracking Optimization for Traffic Data Collection and Analysis

Despite the extensive studies on the performance of video sensors and computer vision algorithms, calibration of these systems is usually done by trial and error using small datasets and incomplete metrics such as simple detection rates. There is a widespread lack of systematic calibration of tracking parameters in the literature. This study proposes an improvement in automatic traffic data collection through the optimization of tracking parameters using a genetic algorithm by comparing tracked road user trajectories to manually annotated ground truth data using the Multiple Object Tracking Accuracy (MOTA) as the fitness function. The optimization procedure is first performed on a given dataset and then validated by applying the resulting parameters on a separate dataset. A number of problematic tracking and visibility conditions are tested using five different camera views selected based on differences in weather conditions, camera resolution, camera angle, tracking distance, and camera site properties. The transferability of the optimized parameters is verified by evaluating the performance of the optimized parameters across these data samples. Results indicate that there are significant improvements to be made in the parametrization. Winter weather conditions require a specialized and distinct set of parameters to reach an acceptable level of performance, while higher resolution cameras have a lower sensitivity to the optimization process and perform well with most sets of parameters. Regarding the impact on traffic variables, average spot speeds are found to be insensitive to MOTA while traffic counts are strongly correlated.