Multiple Object Trackers in OpenCV: A Benchmark
This work provides a comparative analysis for practitioners using OpenCV in applications like autonomous vehicles and surveillance, but it is incremental as it applies existing methods to standard data.
The paper benchmarks 7 multiple object trackers from OpenCV on the MOT20 dataset, reporting results using MOTA and MOTP metrics to address questions about tracker selection and evaluation.
Object tracking is one of the most important and fundamental disciplines of Computer Vision. Many Computer Vision applications require specific object tracking capabilities, including autonomous and smart vehicles, video surveillance, medical treatments, and many others. The OpenCV as one of the most popular libraries for Computer Vision includes several hundred Computer Vision algorithms. Object tracking tasks in the library can be roughly clustered in single and multiple object trackers. The library is widely used for real-time applications, but there are a lot of unanswered questions such as when to use a specific tracker, how to evaluate its performance, and for what kind of objects will the tracker yield the best results? In this paper, we evaluate 7 trackers implemented in OpenCV against the MOT20 dataset. The results are shown based on Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) metrics.