TY - GEN
T1 - Integrating Multiple Inferences for Vehicle Detection by Focusing on Challenging Test Sets
AU - Lee, Jong Taek
AU - Baek, Jang Woon
AU - Moon, Kiyoung
AU - Lim, Kil Taek
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Due to recent advances in object detection with the help of deep convolutional neural networks and region proposal methods, object detection systems have become practical in numerous fields with high accuracy. This paper presents a method for vehicle detection in videos for automatic traffic monitoring. Compared to general object detection datasets such as the PascalVOC and MS-COCO, traffic surveillance datasets such as the UA-DETRAC dataset have different challenging issues: high variation of object size, severe occlusion, dissimilarity between training set and test set. To overcome these difficulties, we employ an unsupervised integration of multiple instances of an image by analyzing video sequences. We applied Faster R-CNN with Neural Architecture Search (NAS) framework as a base network. We achieved 85.76% mAP on the UA-DETRAC detection test set, and outperformed the winner method of the AVSS 2017 challenge on Advanced Traffic Monitoring by 9.19%.
AB - Due to recent advances in object detection with the help of deep convolutional neural networks and region proposal methods, object detection systems have become practical in numerous fields with high accuracy. This paper presents a method for vehicle detection in videos for automatic traffic monitoring. Compared to general object detection datasets such as the PascalVOC and MS-COCO, traffic surveillance datasets such as the UA-DETRAC dataset have different challenging issues: high variation of object size, severe occlusion, dissimilarity between training set and test set. To overcome these difficulties, we employ an unsupervised integration of multiple instances of an image by analyzing video sequences. We applied Faster R-CNN with Neural Architecture Search (NAS) framework as a base network. We achieved 85.76% mAP on the UA-DETRAC detection test set, and outperformed the winner method of the AVSS 2017 challenge on Advanced Traffic Monitoring by 9.19%.
UR - http://www.scopus.com/inward/record.url?scp=85063270937&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2018.8639125
DO - 10.1109/AVSS.2018.8639125
M3 - Conference contribution
AN - SCOPUS:85063270937
T3 - Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
BT - Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018
Y2 - 27 November 2018 through 30 November 2018
ER -