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Can I read Pedestrian Tracking in UAV Images With Kalman Filter Motion Estimator and Correlation Filter on EtoBox?
Pedestrian Tracking in UAV Images With Kalman Filter Motion Estimator and Correlation Filter by Ranjeet Kumar; Alok Kanti Deb is a Engineering article available to read on EtoBox.
What is Pedestrian Tracking in UAV Images With Kalman Filter Motion Estimator and Correlation Filter about?
Visual object tracking is one of the significant parts of systems varying from autonomous driving to drone-based surveillance/tracking. Discriminative correlation filter (DCF) based trackers have proved their prominence in the past few years. These utilize visual information present in images for monitoring. This work uses the Kalman Filter (KF) to derive a motion estimation model. It combines it with two DCFbased trackers, namely Kernelized Correlation Filter (KCF) and Discriminative Correlation Filter with Channel and Spatial Reliability (CSRDCF) for pedestrian tracking. Camera motioncompensated versions of the trackers are also presented. The performance of the proposed methodology is presented in terms of Success Rate (SR) and Precision (P). Real-time power consumption, memory occupancy, and speed of trackers on the Jetson Nano (ARM Cortex-A57, 4GB RAM) board have been presented.
Who reads Pedestrian Tracking in UAV Images With Kalman Filter Motion Estimator and Correlation Filter?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Ranjeet Kumar; Alok Kanti Deb
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)