Abstract
Satellite-based video enables potential vehicle monitoring and tracking for urban traffic management. However, due to the tiny size of moving vehicles and cluttered background, it is difficult to distinguish actual targets from random noise and pseudo-moving objects, resulting in low detection accuracy. In contrast to the currently overused deep-learning-based methods, this study takes full advantage of the geometric properties of vehicle tracklets (segments of moving object trajectory) and proposes a tracklet-feature-based method that can achieve high precision and high recall. The approach is a two-step strategy: (1) smoothing filtering is used to suppress noise, and then a non-parametric-based background subtracting model is applied for obtaining preliminary recognition results with high recall but low precision; and (2) generated tracklets are used to discriminate between true and false vehicles by tracklet feature classification. Experiments and evaluations were performed on SkySat and ChangGuang acquired videos, showing that our method can improve precision and retain high recall, outperforming some classical and deep-learning methods from previously published literature.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference34 articles.
1. Kopsiaftis, G., and Karantzalos, K. (2015, January 26–31). Vehicle Detection and Traffic Density Monitoring from Very High Resolution Satellite Video Data. Proceedings of the IGARSS, Milan, Italy.
2. Needles in a Haystack: Tracking City-Scale Moving Vehicles From Continuously Moving Satellite;Ao;IEEE Trans. on Image Process.,2020
3. Moving Vehicle Detection, Tracking and Traffic Parameter Estimation from a Satellite Video: A Perspective on a Smarter City;Ahmadi;Null,2019
4. Shaikh, S.H., Saeed, K., and Chaki, N. (2014). Moving Object Detection Using Background Subtraction, Springer International Publishing. SpringerBriefs in Computer Science.
5. Friedman, N., and Russell, S. (1997). Image Segmentation in Video Sequences: A Probabilistic Approach. arXiv.