VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results

Zhu, Pengfei; Wen, Longyin; Du, Dawei; Bian, Xiao; Ling, Haibin; Hu, Qinghua; Wu, Haotian; Nie, Qinqin; Cheng, Hao; Liu, Chenfeng; Liu, Xiaoyu; Ma, Wenya; Wang, Lianjie; Schumann, Arne; Wang, Dan; Ortego, Diego; Luna, Elena; Michail, Emmanouil; Bochinski, Erik; Feng, Nan; Bunyak, Filiz; Zhang, Gege; Seetharaman, Guna; Li, Guorong; Yu, Hongyang; Kompatsiaris, Ioannis; Zhao, Jianfei; Gao, Jie; Martínez, José María Álvarez; SanMiguel, Juan C.; Palaniappan, Kannappan; Avgerinakis, Konstantinos; Sommer, Lars; Lauer, Martin; Liu, Mengkun; Al-Shakarji, Noor M.; Acatay, Oliver; Giannakeris, Panagiotis; Zhao, Qijie; Ma, Qingwei; Huang, Qingming; Vrochidis, Stefanos; Sikora, Thomas; Senst, Tobias; Song, Wei; Tian, Wei; Zhang, Wenhua; Zhao, Yanyun; Bai, Yidong; Wu, Yuehao; Wang, Yongtao; Li, Yuxuan; Pi, Zhaoliang; Ma, Zhiming

doi:10.1007/978-3-030-11021-5_29

Cited by 62 publications

(47 citation statements)

References 35 publications

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“…What's more, our method achieves the highest accuracy in all categories, which proves the effectiveness of our method strongly. More experimental results and analysis on test set can refer to VisDrone-VDT2018 [47] and VisDrone-MOT2019 [48].…”

Section: Results On Test Setmentioning

confidence: 99%

Multiple Object Tracking with Motion and Appearance Cues

Liu

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Due to better video quality and higher frame rate, the performance of multiple object tracking issues has been greatly improved in recent years. However, in real application scenarios, camera motion and noisy per frame detection results degrade the performance of trackers significantly. High-speed and high-quality multiple object trackers are still in urgent demand. In this paper, we propose a new multiple object tracker following the popular tracking-by-detection scheme. We tackle the camera motion problem with an optical flow network and utilize an auxiliary tracker to deal with the missing detection problem. Besides, we use both the appearance and motion information to improve the matching quality. The experimental results on the VisDrone-MOT dataset show that our approach can improve the performance of multiple object tracking significantly while achieving a high efficiency.

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Section: Results On Test Setmentioning

confidence: 99%

Multiple Object Tracking with Motion and Appearance Cues

Liu

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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“…In this section, experimental results, as well as comparative evaluation from the application of the proposed object detection method, are presented. For the evaluation, the 'KITTI' [9], 'V-KITTI' [7], 'Visdrone' [30], 'Cityscapes' [2], 'Berkeley Deep Drive' [29] as well as the 'UDacity' [4] datasets were used.…”

Section: Object Detection Datasetsmentioning

confidence: 99%

Flow R-CNN: Flow-Enhanced Object Detection

Psaltis

Dimou

Álvarez

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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This work addresses the problem of multi-task object detection in an efficient, generic but at the same time simple way, following the recent and highly promising studies in the computer vision field, and more specifically the Region-based CNN (R-CNN) approach. A flowenhanced methodology for object detection is proposed, by adding a new branch to predict an object-level flow field. Following a scheme grounded on neuroscience, a pseudo-temporal motion stream is integrated in parallel to the classification, bounding box regression and segmentation mask prediction branches of Mask R-CNN. Extensive experiments and thorough comparative evaluation provide a detailed analysis of the problem at hand and demonstrate the added value of the involved object-level flow branch. The overall proposed approach achieves improved performance in the six currently broadest and most challenging publicly available semantic urban scene understanding datasets, surpassing the region-based baseline method.

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“…48 Most of the developed methods mainly used frontal view data set. Some of the researchers developed object tracking method, [49][50][51][52] but they use aerial or remote sensing images. Few used deep learning for detection and counting of person in the overhead view.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional neural network–based person tracking using overhead views

Ahmad

Khan

Qayum

et al. 2020

International Journal of Distributed Sensor Networks

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In video surveillance, person tracking is considered as challenging task. Numerous computer vision, machine and deep learning–based techniques have been developed in recent years. Majority of these techniques are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking. The network layers of convolutional neural network models trained on a number of images or video sequences improve speed and accuracy of object tracking. In this work, the generalization performance of existing pre-trained deep learning models have investigated for overhead view person detection and tracking, under different experimental conditions. The object tracking method Generic Object Tracking Using Regression Networks (GOTURN) which has been yielding outstanding tracking results in recent years is explored for person tracking using overhead views. This work mainly focused on overhead view person tracking using Faster region convolutional neural network (Faster-RCNN) in combination with GOTURN architecture. In this way, the person is first identified in overhead view video sequences and then tracked using a GOTURN tracking algorithm. Faster-RCNN detection model achieved the true detection rate ranging from 90% to 93% with a minimum false detection rate up to 0.5%. The GOTURN tracking algorithm achieved similar results with the success rate ranging from 90% to 94%. Finally, the discussion is made on output results along with future direction.

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VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results

Cited by 62 publications

References 35 publications

Multiple Object Tracking with Motion and Appearance Cues

Multiple Object Tracking with Motion and Appearance Cues

Flow R-CNN: Flow-Enhanced Object Detection

Convolutional neural network–based person tracking using overhead views

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