A Sensitivity Analysis (and Practitioners’ Guide to) of DeepSORT for Low Frame Rate Video

Simple Online and Real-time Tracking with a Deep Association Metric (DeepSORT) has been widely used due to its simplicity and strong empirical performance on Multiple Object Tracking (MOT). However, in real-world applications involving low frame rate (LFR) videos, DeepSORT requires practitioners to tune its hyperparameters to handle abrupt changes in motion and reduce erroneous tracks. Additionally, it is currently unknown how sensitive the DeepSORT's performance is to changes in these hyperparameters for LFR videos. Thus we conduct a sensitivity analysis of DeepSORT to explore the effect of these hyperparameters on the overall performance in LFR videos. Our main aim is to understand the impact of hyperparameter and identify crucial choices for DeepSORT in LFR-MOT. We finally present practical recommendations based on our extensive empirical study for those interested in getting the most out DeepSORT for LFR-MOT in real-world settings.