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Works69 from public data
- 1,147
- A Pose-Sensitive Embedding for Person Re-identification with Expanded Cross Neighborhood Re-ranking526
This paper proposes an effective approach that incorporates both the fine and coarse pose information of the person to learn a discriminative embedding and shows that both the learned representation and the re-ranking method achieve state-of-the-art performance on a number of challenging surveillance image and video datasets.
- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results259
Based on the selected 29 robust detection methods, the experimental results comprehensively are discussed, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection.
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- Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge153
Three different original approaches based on different deep learning strategies that competed in a grand challenge on the “Drone vs. Bird” detection problem are proposed and compared and reveal that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.
- VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results133
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.
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- Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model100
This paper shows in an extensive evaluation on three challenging datasets that the proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.
- VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results81
A large-scale video object detection and tracking dataset, which consists of 79 video clips with about 1.5 million annotated bounding boxes in 33, 366 frames, and the evaluation protocol of the VisDrone-VDT2018 challenge and the results of the algorithms on the benchmark dataset are presented.
- SkyScapes Fine-Grained Semantic Understanding of Aerial Scenes81
A novel multi-task model is proposed, which incorporates semantic edge detection and is better tuned for feature extraction from a wide range of scales, which achieves notable improvements over the baselines in region outlines and level of detail on both tasks.
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- Drone-vs-Bird Detection Challenge at IEEE AVSS201968
The second edition of the “drone-vs-bird” detection challenge is presented, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.
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- Human Pose Estimation for Real-World Crowded Scenarios45
This work explores methods to optimize pose estimation for human crowds, focusing on challenges introduced with larger scale crowds like people in close proximity to each other, mutual occlusions, and partial visibility of people due to the environment.
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- The Drone-vs-Bird Detection Grand Challenge at ICASSP 2023: A Review of Methods and Results39
The paper illustrates the results achieved by the teams that successfully participated in the 2023 Drone-vs-Bird challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing.
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- UnrealGT: Using Unreal Engine to Generate Ground Truth Datasets30
This work proposes a framework for synthetic test data generation utilizing the Unreal Engine, with which a CNN for object detection as well as a V-SLAM algorithm are trained and evaluated and shows that the generated synthetic data can be used as an alternative to real data.
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- VisDrone-MOT2020: The Vision Meets Drone Multiple Object Tracking Challenge Results27
The VisDrone-MOT2020 consists of 79 challenging video sequences, including 56 videos for training, 7 videos for validation and 17 videos for evaluation, which feature 12 participating MOT algorithms.
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- A systematic evaluation of recent deep learning architectures for fine-grained vehicle classification24
This work investigates the suitability of several recent landmark convolutional neural network (CNN) architectures, which have shown top results on large scale image classification tasks, for the task of fine-grained classification of vehicles, and compares the performance of the networks VGG16, several ResNets, Inception architectures, the recent DenseNets and MobileNet.
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- Extended image differencing for change detection in UAV video mosaics23
The results show that for video mosaics this task is more difficult than for single video frames and the image registration is extended by estimating an elastic transformation using a thin plate spline approach, which is useful for interactive image exploitation due to a larger scene coverage.
- Deep learning prototype domains for person re-identification20
This work forms a deep learning based novel approach to automatic prototype-domain discovery for domain perceptive person re-id that outperforms state-of-the-art unsupervised methods significantly and is competitive against supervised methods which use labelled test domain data.
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- Person re-identification across aerial and ground-based cameras by deep feature fusion18
This work investigates the suitability of features, which were shown to give robust results for re- identification in camera networks, for the task of re-identifying persons between a camera network and a mobile aerial camera, and applies hand-crafted region covariance features and features extracted by convolu- tional neural networks which were learned on separate data.
- Deep learning-based drone detection in infrared imagery with limited training data14
The results show that suitable pre-processing of optical data can significantly improve the resulting accuracy and performs much better than training solely on limited infrared data.
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- An image processing pipeline for long range UAV detection13
This work presents an image processing pipeline which is capable of tracking very small point targets in an overview camera, adjusting a tilting unit with a mounted zoom camera (PTZ system) to locations of interest and classifying the spotted object in this more detailed camera view.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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