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14 results on '"Guanchen Ding"'

6. The First Challenge on Moving Object Detection and Tracking in Satellite Videos: Methods and Results.

Author

Yulan Guo, Qian Yin, Qingyong Hu, Feng Zhang, Chao Xiao, Ye Zhang, Hanyun Wang, Chenguang Dai, Jian Yang, Zhuang Zhou, Weilong Guo, Xiyu Qi, Kelong Tu, Cong Xu, Shudan Zhu, Lai Chen, Bin Lin, Chaocan Xue, Jinlei Zheng, Limei Qin, Ying Li 0017, Manqi Zhao, Lu Ruan 0003, Mingpeng Cui, Guanchen Ding, Guangwei Jiang, Zhenzhong Chen, Yuhan Sun, Kaiyang Cao, Lingyu Kong, Shaodong Chen, Zhicheng Zhao, Qin Shen, Lei Liu, Chenglong Li 0002, and Yun Xiao 0003

Published
2022
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7. Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report.

Author

Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou 0015, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li 0103, Xingdong Zhang, Jiawei Zhang 0002, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu 0017, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu 0003, Zhenzhong Chen, Gen Li 0011, Yuanfan Zhang, Lei Sun 0009, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu 0040, Qiang Liu 0026, Dahai Yu, Ao Li 0007, Lei Luo 0003, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu 0031, Garas Gendy, Nabil Sabor, Jingchao Hou, and Guanghui He

Published
2022
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9. VisDrone-CC2021: The Vision Meets Drone Crowd Counting Challenge Results.

Author

Zhihao Liu, Zhijian He, Lujia Wang 0001, Wenguan Wang, Yixuan Yuan, Dingwen Zhang, Jinglin Zhang, Pengfei Zhu 0001, Luc Van Gool, Junwei Han, Steven C. H. Hoi, Qinghua Hu, Ming Liu 0001, Junwen Pan, Baoqun Yin, Binyu Zhang, Chengxin Liu, Ding Ding, Dingkang Liang, Guanchen Ding, Hao Lu 0003, Hui Lin, Jingyuan Chen, Jiong Li, Liang Liu, Lin Zhou, Min Shi 0004, Qianqian Yang, Qing He, Sifan Peng, Wei Xu 0017, Wenwei Han, Xiang Bai, Xiwu Chen, Yabin Wang, Yinfeng Xia, Yiran Tao, Zhenzhong Chen, and Zhiguo Cao 0001

Published
2021
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12. VisDrone-CC2021: The Vision Meets Drone Crowd Counting Challenge Results

Author

Guanchen Ding, Junwei Han, Ding Ding, Hui Lin, Junwen Pan, Xiang Bai, Jingyuan Chen, Dingwen Zhang, Yinfeng Xia, Zhenzhong Chen, Qing He, Qianqian Yang, Yabin Wang, Ming Liu, Liang Dingkang, Wenguan Wang, Hao Lu, Sifan Peng, Jiong Li, Luc Van Gool, Yiran Tao, Zhiguo Cao, Zhijian He, Lin Zhou, Xu Wei, Yixuan Yuan, Baoqun Yin, Binyu Zhang, Pengfei Zhu, Steven C. H. Hoi, Lujia Wang, Chengxin Liu, Jinglin Zhang, Xiwu Chen, Qinghua Hu, Wenwei Han, Liang Liu, Min Shi, and Zhihao Liu

Subjects
Computer science, business.industry, Computer vision, Artificial intelligence, business, Drone, Crowd counting
Published
2021
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13. Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing

Author

Tianyi Gao, Jingyuan Chen, Zhenzhong Chen, Hao Cai, Yuchen Yang, Guanchen Ding, Zhe Zhang, Kangmin Xu, Wenwei Han, and Wanping Ouyang

Subjects
FOS: Computer and information sciences, Vehicle tracking system, Computer Science - Artificial Intelligence, Backtracking, business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), Anomaly (natural sciences), Computer Science - Computer Vision and Pattern Recognition, Tracing, Vehicle dynamics, Artificial Intelligence (cs.AI), Region of interest, Anomaly detection, Computer vision, Artificial intelligence, business, Intelligent transportation system
Abstract

Traffic anomaly detection has played a crucial role in Intelligent Transportation System (ITS). The main challenges of this task lie in the highly diversified anomaly scenes and variational lighting conditions. Although much work has managed to identify the anomaly in homogenous weather and scene, few resolved to cope with complex ones. In this paper, we proposed a dual-modality modularized methodology for the robust detection of abnormal vehicles. We introduced an integrated anomaly detection framework comprising the following modules: background modeling, vehicle tracking with detection, mask construction, Region of Interest (ROI) backtracking, and dual-modality tracing. Concretely, we employed background modeling to filter the motion information and left the static information for later vehicle detection. For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies. Besides, we utilized the frame difference and tracking results to identify the road and obtain the mask. In addition, we introduced multiple similarity estimation metrics to refine the anomaly period via backtracking. Finally, we proposed a dual-modality bilateral tracing module to refine the time further. The experiments conducted on the Track 4 testset of the NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and 3.4039 root mean square error (RMSE), indicating the effectiveness of our framework., Comment: 9 pages, 5 figures, accepted to CVPRW 2021

Published
2021
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14. Drone-Based Car Counting via Density Map Learning

Author

Yujia Guo, Jingxian Huang, Sihan Wang, Daiqin Yang, Tao Wang, Yunfei Zhang, and Guanchen Ding

Subjects
Ground truth, Computer science, business.industry, 020208 electrical & electronic engineering, Feature extraction, 02 engineering and technology, Drone, Task (computing), symbols.namesake, Kernel (statistics), 0202 electrical engineering, electronic engineering, information engineering, Gaussian function, symbols, 020201 artificial intelligence & image processing, Computer vision, Point (geometry), Artificial intelligence, business, Focus (optics)
Abstract

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

Published
2020
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