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AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features

Kraus, Maximilian and Azimi, Seyedmajid and Ercelik, Emec and Bahmanyar, Reza and Reinartz, Peter and Knoll, Alois (2021) AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features. In: International Conference on Pattern Recognition, pp. 2454-2461. ICPR 2020, Milan, Italy. doi: 10.1109/ICPR48806.2021.9413031.

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Official URL: https://ieeexplore.ieee.org/document/9413031


Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4×4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. We believe that AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency.

Item URL in elib:https://elib.dlr.de/136057/
Document Type:Conference or Workshop Item (Speech)
Title:AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Azimi, SeyedmajidUNSPECIFIEDhttps://orcid.org/0000-0002-6084-2272
Bahmanyar, RezaUNSPECIFIEDhttps://orcid.org/0000-0002-6999-714X
Reinartz, PeterUNSPECIFIEDhttps://orcid.org/0000-0002-8122-1475
Date:January 2021
Journal or Publication Title:International Conference on Pattern Recognition
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 2454-2461
Keywords:Aerial Imagery; Deep Neural Networks; Pedestrian Tracking; Vehicle Tracking
Event Title:ICPR 2020
Event Location:Milan, Italy
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bahmanyar, Gholamreza
Deposited On:24 Sep 2020 11:30
Last Modified:25 Aug 2021 10:56

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