TY  - JOUR
PY  - 2021//
TI  - Efficient single-shot multi-object tracking for vehicles in traffic scenarios
JO  - Sensors (Basel)
A1  - Lee, Youngkeun
A1  - Lee, Sang-Ha
A1  - Yoo, Jisang
A1  - Kwon, Soonchul
SP  - e6358
EP  - e6358
VL  - 21
IS  - 19
N2  - Multi-object tracking is a significant field in computer vision since it provides essential information for video surveillance and analysis. Several different deep learning-based approaches have been developed to improve the performance of multi-object tracking by applying the most accurate and efficient combinations of object detection models and appearance embedding extraction models. However, two-stage methods show a low inference speed since the embedding extraction can only be performed at the end of the object detection. To alleviate this problem, single-shot methods, which simultaneously perform object detection and embedding extraction, have been developed and have drastically improved the inference speed. However, there is a trade-off between accuracy and efficiency. Therefore, this study proposes an enhanced single-shot multi-object tracking system that displays improved accuracy while maintaining a high inference speed. With a strong feature extraction and fusion, the object detection of our model achieves an AP score of 69.93% on the UA-DETRAC dataset and outperforms previous state-of-the-art methods, such as FairMOT and JDE. Based on the improved object detection performance, our multi-object tracking system achieves a MOTA score of 68.5% and a PR-MOTA score of 24.5% on the same dataset, also surpassing the previous state-of-the-art trackers.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s21196358
ID  - ref1
ER  -