<![CDATA[Multi object tracking (MOT) is a crucial component of modern computer vision applications, ranging from intelligent surveillance to autonomous vehicles. The primary challenge in MOT lies in maintaining identity consistency under conditions of high density and frequent occlusion, while also ensuring computational efficiency for real time deployment on resource constrained devices. This paper introduces LITE BoostTrack, a hybrid architecture that combines the confidence scaling based association mechanism of BoostTrack with the lightweight feature extraction strategy of the Lightweight Integrated Tracking and Embedding (LITE) framework. By leveraging internal features from the YOLOv8 detector without relying on an external Re Identification module, the proposed approach reduces computational burden while preserving robustness in identity association. Experiments were conducted on the MOT20 benchmark using standard evaluation metrics, namely HOTA, MOTA, IDF1, IDSW, and FPS, to comprehensively assess both tracking accuracy and runtime efficiency. The results demonstrate that LITE BoostTrack achieves competitive accuracy, with a HOTA of 27.32 and an IDF1 of 37.49, which are nearly equivalent to the original BoostTrack. At the same time, it delivers a substantial improvement in runtime efficiency, reaching 13.23 FPS, almost twice the speed of standard BoostTrack. These findings confirm that efficiency optimization in MOT can be achieved through architectural reengineering that exploits detector internal features without the need for additional deep modules. LITE BoostTrack therefore represents a balanced and practical solution that combines accuracy with efficiency, making it well suited for real time applications in edge computing and resource constrained environments.]]>
