SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects

• URL: http://arxiv.org/abs/2410.20079v1
• Date: Sat, 26 Oct 2024 05:09:20 GMT
• Title: SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects
• Authors: InPyo Song, Jangwon Lee,
• Abstract summary: This paper addresses the problem of multi-object tracking in Unmanned Aerial Vehicle (UAV) footage. It plays a critical role in various UAV applications, including traffic monitoring systems and real-time suspect tracking by the police. We propose a new tracking strategy, which initiates the tracking of target objects from low-confidence detections.
• Score: 2.9803250365852443
• License:
• Abstract: This paper addresses the problem of multi-object tracking in Unmanned Aerial Vehicle (UAV) footage. It plays a critical role in various UAV applications, including traffic monitoring systems and real-time suspect tracking by the police. However, this task is highly challenging due to the fast motion of UAVs, as well as the small size of target objects in the videos caused by the high-altitude and wide angle views of drones. In this study, we thus introduce a simple yet more effective method compared to previous work to overcome these challenges. Our approach involves a new tracking strategy, which initiates the tracking of target objects from low-confidence detections commonly encountered in UAV application scenarios. Additionally, we propose revisiting traditional appearance-based matching algorithms to improve the association of low-confidence detections. To evaluate the effectiveness of our method, we conducted benchmark evaluations on two UAV-specific datasets (VisDrone2019, UAVDT) and one general object tracking dataset (MOT17). The results demonstrate that our approach surpasses current state-of-the art methodologies, highlighting its robustness and adaptability in diverse tracking environments. Furthermore, we have improved the annotation of the UAVDT dataset by rectifying several errors and addressing omissions found in the original annotations. We will provide this refined version of the dataset to facilitate better benchmarking in the field.

Related papers

• VOVTrack: Exploring the Potentiality in Videos for Open-Vocabulary Object Tracking [61.56592503861093]
  This issue amalgamates the complexities of open-vocabulary object detection (OVD) and multi-object tracking (MOT) Existing approaches to OVMOT often merge OVD and MOT methodologies as separate modules, predominantly focusing on the problem through an image-centric lens. We propose VOVTrack, a novel method that integrates object states relevant to MOT and video-centric training to address this challenge from a video object tracking standpoint.
  arXiv  Detail & Related papers  (2024-10-11T05:01:49Z)
• Learning Motion Blur Robust Vision Transformers with Dynamic Early Exit for Real-Time UAV Tracking [14.382072224997074]
  Single-stream architectures utilizing pre-trained ViT backbones offer improved performance, efficiency, and robustness. We boost the efficiency of this framework by tailoring it into an adaptive framework that dynamically exits Transformer blocks for real-time UAV tracking. We also improve the effectiveness of ViTs in handling motion blur, a common issue in UAV tracking caused by the fast movements of either the UAV, the tracked objects, or both.
  arXiv  Detail & Related papers  (2024-07-07T14:10:04Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow [27.621524657473945]
  Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object appearance, and very large and/or irregular motion. We propose FOLT to mitigate these problems and reach fast and accurate MOT in UAV view.
  arXiv  Detail & Related papers  (2023-08-14T15:24:44Z)
• SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking [12.447854608181833]
  This work presents a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation. A lightweight saliency filtering Transformer further refines saliency information and increases the focus on appearance information.
  arXiv  Detail & Related papers  (2023-03-08T05:01:00Z)
• Vision-based Anti-UAV Detection and Tracking [18.307952561941942]
  Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. We propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV. It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences.
  arXiv  Detail & Related papers  (2022-05-22T15:21:45Z)
• Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking [15.38386172273694]
  This work proposes a novel adaptive adversarial attack approach, i.e., Ad$2$Attack, against UAV object tracking. A novel optimization function is proposed for balancing the imperceptibility and efficiency of the attack. Experiments on several well-known benchmarks and real-world conditions show the effectiveness of our attack method.
  arXiv  Detail & Related papers  (2022-03-03T05:00:32Z)
• Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking [59.06167734555191]
  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. We consider the task of tracking UAVs, providing rich information such as location and trajectory. We propose a dataset, Anti-UAV, with more than 300 video pairs containing over 580k manually annotated bounding boxes.
  arXiv  Detail & Related papers  (2021-01-21T07:00:15Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• TAO: A Large-Scale Benchmark for Tracking Any Object [95.87310116010185]
  Tracking Any Object dataset consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. We ask annotators to label objects that move at any point in the video, and give names to them post factum. Our vocabulary is both significantly larger and qualitatively different from existing tracking datasets.
  arXiv  Detail & Related papers  (2020-05-20T21:07:28Z)
• Tracking Road Users using Constraint Programming [79.32806233778511]
  We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem. Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.
  arXiv  Detail & Related papers  (2020-03-10T00:04:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.