Dense Crowds Detection and Surveillance with Drones using Density Maps

• URL: http://arxiv.org/abs/2003.08766v1
• Date: Tue, 3 Mar 2020 02:05:47 GMT
• Title: Dense Crowds Detection and Surveillance with Drones using Density Maps
• Authors: Javier Gonzalez-Trejo, Diego Mercado-Ravell
• Abstract summary: In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using drones

Related papers

• DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection [1.2564343689544843]
  Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable. Our work improves on the previous approach by combining several improvements. The proposed technique won 1st Place in the Drone vs. Bird Challenge.
  arXiv  Detail & Related papers  (2024-06-30T20:49:56Z)
• A Two-Dimensional Deep Network for RF-based Drone Detection and Identification Towards Secure Coverage Extension [7.717171534776764]
  We use Short-Time Fourier Transform to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset.
  arXiv  Detail & Related papers  (2023-08-26T15:43:39Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• Track Boosting and Synthetic Data Aided Drone Detection [0.0]
  Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data. Our results indicate that augmenting the real data with an optimal subset of synthetic data can increase the performance.
  arXiv  Detail & Related papers  (2021-11-24T10:16:27Z)
• A dataset for multi-sensor drone detection [67.75999072448555]
  The use of small and remotely controlled unmanned aerial vehicles (UAVs) has increased in recent years. Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset. We contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files.
  arXiv  Detail & Related papers  (2021-11-02T20:52:03Z)
• Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark [97.07865343576361]
  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.
  arXiv  Detail & Related papers  (2021-05-06T04:46:14Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)
• Drone LAMS: A Drone-based Face Detection Dataset with Large Angles and Many Scenarios [2.4378845585726903]
  The proposed dataset captured images from 261 videos with over 43k annotations and 4.0k images with pitch or yaw angle in the range of -90deg to 90deg. Drone LAMS showed significant improvement over currently available drone-based face detection datasets in terms of detection performance.
  arXiv  Detail & Related papers  (2020-11-16T02:26:05Z)
• 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)
• Multi-Drone based Single Object Tracking with Agent Sharing Network [74.8198920355117]
  Multi-Drone single Object Tracking dataset consists of 92 groups of video clips with 113,918 high resolution frames taken by two drones and 63 groups of video clips with 145,875 high resolution frames taken by three drones. Agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones.
  arXiv  Detail & Related papers  (2020-03-16T03:27:04Z)

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.