This paper presents a pragmatic approach to multiple object tracking (MOT) that focuses on efficient online and real-time applications. The authors highlight the importance of detection quality in tracking performance, noting that changing the detector can improve tracking accuracy by up to 18.9%. Despite using basic techniques like the Kalman Filter and Hungarian algorithm, the proposed method achieves comparable accuracy to state-of-the-art online trackers while maintaining a high update rate of 260 Hz, over 20 times faster than other advanced trackers. The paper introduces a lean implementation of a tracking-by-detection framework, where objects are detected each frame and represented as bounding boxes. This approach is designed for online tracking, focusing on efficiency and real-time performance, particularly for applications like pedestrian tracking in autonomous vehicles. The method uses a linear constant velocity model for motion prediction and the Hungarian algorithm for data association. The authors also discuss the trade-offs between accuracy and speed, emphasizing that while accurate trackers are often slow, their proposed method combines both speed and accuracy effectively. The main contributions of the paper include leveraging CNN-based detection in MOT, presenting a pragmatic tracking approach based on classical methods, and open-sourcing the code to establish a baseline for research and practical applications. The paper evaluates the proposed method on a recent MOT benchmark, demonstrating superior performance in terms of accuracy and speed compared to several baseline trackers.This paper presents a pragmatic approach to multiple object tracking (MOT) that focuses on efficient online and real-time applications. The authors highlight the importance of detection quality in tracking performance, noting that changing the detector can improve tracking accuracy by up to 18.9%. Despite using basic techniques like the Kalman Filter and Hungarian algorithm, the proposed method achieves comparable accuracy to state-of-the-art online trackers while maintaining a high update rate of 260 Hz, over 20 times faster than other advanced trackers. The paper introduces a lean implementation of a tracking-by-detection framework, where objects are detected each frame and represented as bounding boxes. This approach is designed for online tracking, focusing on efficiency and real-time performance, particularly for applications like pedestrian tracking in autonomous vehicles. The method uses a linear constant velocity model for motion prediction and the Hungarian algorithm for data association. The authors also discuss the trade-offs between accuracy and speed, emphasizing that while accurate trackers are often slow, their proposed method combines both speed and accuracy effectively. The main contributions of the paper include leveraging CNN-based detection in MOT, presenting a pragmatic tracking approach based on classical methods, and open-sourcing the code to establish a baseline for research and practical applications. The paper evaluates the proposed method on a recent MOT benchmark, demonstrating superior performance in terms of accuracy and speed compared to several baseline trackers.