TY - GEN
T1 - Vehicle tracking using deep SORT with low confidence track filtering
AU - Hou, Xinyu
AU - Wang, Yi
AU - Chau, Lap Pui
N1 - Funding Information:
We wish to acknowledge the funding for this project from Nanyang Technological University under the Undergraduate Research Experience on CAmpus (URECA) programme.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Multi-object tracking (MOT) becomes an attractive topic due to its wide range of usability in video surveillance and traffic monitoring. Recent improvements on MOT has focused on tracking-by-detection manner. However, as a relatively complicated and integrated computer vision mission, state-of-the-art tracking-by-detection techniques are still suffering from issues such as a large number of false-positive tracks. To reduce the effect of unreliable detections on vehicle tracking, in this paper, we propose to incorporate a low confidence track filtering into the Simple Online and Realtime Tracking with a Deep association metric (Deep SORT) algorithm. We present a self-generated UA-DETRAC vehicle re-identification dataset which can be used to train the convolutional neural network of Deep SORT for data association. We evaluate our proposed tracker on UA-DETRAC test dataset. Experimental results show that the proposed method can improve the original Deep SORT algorithm with a significant margin. Our tracker outperforms the state-of-the-art online trackers and is comparable with batch-mode trackers.
AB - Multi-object tracking (MOT) becomes an attractive topic due to its wide range of usability in video surveillance and traffic monitoring. Recent improvements on MOT has focused on tracking-by-detection manner. However, as a relatively complicated and integrated computer vision mission, state-of-the-art tracking-by-detection techniques are still suffering from issues such as a large number of false-positive tracks. To reduce the effect of unreliable detections on vehicle tracking, in this paper, we propose to incorporate a low confidence track filtering into the Simple Online and Realtime Tracking with a Deep association metric (Deep SORT) algorithm. We present a self-generated UA-DETRAC vehicle re-identification dataset which can be used to train the convolutional neural network of Deep SORT for data association. We evaluate our proposed tracker on UA-DETRAC test dataset. Experimental results show that the proposed method can improve the original Deep SORT algorithm with a significant margin. Our tracker outperforms the state-of-the-art online trackers and is comparable with batch-mode trackers.
UR - http://www.scopus.com/inward/record.url?scp=85076349223&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2019.8909903
DO - 10.1109/AVSS.2019.8909903
M3 - Conference article published in proceeding or book
AN - SCOPUS:85076349223
T3 - 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
BT - 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
Y2 - 18 September 2019 through 21 September 2019
ER -