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AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features
- Source :
- ICPR
- Publication Year :
- 2020
-
Abstract
- 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 x 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.<br />ICPR 2020
- Subjects :
- Event monitoring
FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
0211 other engineering and technologies
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Computer Science - Computer Vision and Pattern Recognition
ComputerApplications_COMPUTERSINOTHERSYSTEMS
02 engineering and technology
Pedestrian
Tracking (particle physics)
Aerial Imagery
0202 electrical engineering, electronic engineering, information engineering
Computer vision
021101 geological & geomatics engineering
Deep Neural Networks
Pedestrian Tracking
Photogrammetrie und Bildanalyse
Artificial neural network
Pixel
business.industry
Deep learning
Frame rate
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
business
Vehicle Tracking
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICPR
- Accession number :
- edsair.doi.dedup.....9e3a0db00fa98a274f05278ab7630faa