Open AccessPosted Content
Multiple Object Tracking with Motion and Appearance Cues
TL;DR: Wang et al. as mentioned in this paper proposed a new multiple object tracker following the popular tracking-by-detection scheme, which tackles the camera motion problem with an optical flow network and utilizes an auxiliary tracker to deal with the missing detection problem.
read more
Abstract: 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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning
Xinshuo Weng,Yongxin Wang,Yunze Man,Kris M. Kitani +3 more
- 14 Jun 2020
TL;DR: This work proposes two techniques to improve the discriminative feature learning for MOT by introducing a novel feature interaction mechanism by introducing the Graph Neural Network and proposes a novel joint feature extractor to learn appearance and motion features from 2D and 3D space simultaneously.
Deep Learning for UAV-based Object Detection and Tracking: A Survey
TL;DR: A comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods can be found in this article, where the authors outline the challenges, statistics of existing methods, and provide solutions from the perspectives of deep learning-based models in three research topics: object detection from the image and video, and object tracking from the video.
209
•Posted Content
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
TL;DR: This work proposes two techniques to improve the discriminative feature learning for MOT by introducing a novel feature interaction mechanism by introducing the Graph Neural Network and proposes a novel joint feature extractor to learn appearance and motion features from 2D and 3D space simultaneously.
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin,Piotr Dollár,Ross Girshick,Kaiming He,Bharath Hariharan,Serge Belongie +5 more
- 21 Jul 2017
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Focal Loss for Dense Object Detection
Tsung-Yi Lin,Priya Goyal,Ross Girshick,Kaiming He,Piotr Dollár +4 more
- 07 Aug 2017
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
•Posted Content
YOLOv3: An Incremental Improvement.
Joseph Redmon,Ali Farhadi +1 more
TL;DR: The authors present some updates to YOLO!
17.8K
Related Papers (5)
Junmin Ke,Shengting Guo +1 more
- 17 Oct 2020
Ricardo Sanchez-Matilla,Andrea Cavallaro +1 more
- 23 Aug 2020