Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark

• URL: http://arxiv.org/abs/2105.02440v1
• Date: Thu, 6 May 2021 04:46:14 GMT
• Title: Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
• Authors: Longyin Wen, Dawei Du, Pengfei Zhu, Qinghua Hu, Qilong Wang, Liefeng Bo, Siwei Lyu
• Abstract summary: We construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd. We annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. We design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds.
• Score: 97.07865343576361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd, formed by 112 video clips with 33,600 HD frames in various scenarios. Notably, we annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. Meanwhile, we design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds. STNNet is formed by the feature extraction module, followed by the density map estimation heads, and localization and association subnets. To exploit the context information of neighboring objects, we design the neighboring context loss to guide the association subnet training, which enforces consistent relative position of nearby objects in temporal domain. Extensive experiments on our DroneCrowd dataset demonstrate that STNNet performs favorably against the state-of-the-arts.

Related papers

• DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy [33.57923199717605]
  Drone-based crowd tracking faces difficulties in accurately identifying and monitoring objects from an aerial perspective. To address these challenges, we present the Density-aware Tracking (DenseTrack) framework. DenseTrack capitalizes on crowd counting to precisely determine object locations, blending visual and motion cues to improve the tracking of small-scale objects.
  arXiv  Detail & Related papers  (2024-07-24T13:39:07Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition [2.705905918316948]
  Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. We propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets.
  arXiv  Detail & Related papers  (2022-12-07T00:33:40Z)
• Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark [30.078513715446196]
  We build a large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking. This dataset is collected by the Jilin-1 satellite constellation. We establish the first public benchmark for moving object detection and tracking in satellite videos.
  arXiv  Detail & Related papers  (2021-11-25T08:01:41Z)
• Learning to Track with Object Permanence [61.36492084090744]
  We introduce an end-to-end trainable approach for joint object detection and tracking. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
  arXiv  Detail & Related papers  (2021-03-26T04:43:04Z)
• Benchmarking Deep Trackers on Aerial Videos [5.414308305392762]
  In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos.
  arXiv  Detail & Related papers  (2021-03-24T01:45:19Z)
• Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets [96.98888948518815]
  State-of-the-art multi-object tracking(MOT) methods follow the tracking-by-detection paradigm. We propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.
  arXiv  Detail & Related papers  (2020-07-18T19:51:53Z)
• Benchmarking Unsupervised Object Representations for Video Sequences [111.81492107649889]
  We compare the perceptual abilities of four object-centric approaches: ViMON, OP3, TBA and SCALOR. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking. Our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
  arXiv  Detail & Related papers  (2020-06-12T09:37:24Z)
• Detection and Tracking Meet Drones Challenge [131.31749447313197]
  This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations. We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
  arXiv  Detail & Related papers  (2020-01-16T00:11:56Z)

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.