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

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.Comment: ICPR 202

A Scheme for the Detection and Tracking of People Tuned for Aerial Image Sequences

Abstract. This paper addresses the problem of detecting and tracking a large number of individuals in aerial image sequences that have been taken from high altitude. We propose a method which can handle the numerous challenges that are associated with this task and demonstrate its quality on several test sequences. Moreover this paper contains several contributions to improve object detection and tracking in other domains, too. We show how to build an effective object detector in a flexible way which incorporates the shadow of an object and enhanced features for shape and color. Furthermore the performance of the detector is boosted by an improved way to collect background samples for the classifier train-ing. At last we describe a tracking-by-detection method that can handle frequent misses and a very large number of similar objects