At the onset of developing computer vision systems, developers often rely on readily available open-source implementations for trackers, such as SORT and DeepSORT, which can easily be found on the internet.
The main reason for their popularity is that these solutions are presented by the algorithms’ authors themselves and have gathered huge attention from those days. As a result, developers may think that they are the best options available, which is wrong, because despite those implementations are correct, they are not optimized for speed.
When tracking objects in real-time, it is crucial to have a fast and accurate system in place. Slow trackers can cause delays, missed detections, and even wrong classifications, leading to severe consequences in critical applications. For example, in autonomous driving, a slow tracker can cause the car to miss important objects like pedestrians or other vehicles, leading to potentially deadly accidents.
Another issue with slow trackers is that they require more computational power and resources, making them less suitable for low-powered devices or real-time applications. This issue can significantly limit the potential use cases for computer vision systems and make them less applicable for the edge.
Our new article describes the problems which a slow object tracker may cause and how they affect on AI pipeline performance, economy and bottlenecks.