| Title |
Robust Track Management based on Multi-object Tracking to Improve Object Detection |
| Authors |
김민기(Mingi Kim) ; 이동석(Dongseok Lee) ; 최병인(Byungin Choi) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.12.43 |
| Keywords |
Object detection; Multi-object tracking; Template matching; Track management; Data association |
| Abstract |
Recently, deep learning-based object detection and multi-object tracking used in autonomous driving technology have been widely studied. The two technologies consist of one sequence. Taking advantage of this, we propose a robust track management based on multi-object tracking technology that complements the limitations of object detection using only a single image. This paper propose the following method. Hungarian algorithm cost matrix for inter-class using class information,, Track management technique that induces data association through Template Matching, Track update that improves reliability for object detection by utilizing additional information of class and score. Through three types of robust track management, it compensate for non-detection or mis-classification problems that occur in object detection. Additionally, it show stable results in object detection and multi-object tracking. As a result, compared to the model using only object detection, the mAP increase by about 4%, and the precision result increased by about 10%. we were tested in an actual autonomous driving environment and recorded high performance improvement in objects with little learning data or small sizes. In addition, it enabled stable object detection and tracking even in sudden image shaking such as bumps. |