Pedestrian Tracking in UAV Images With Kalman Filter Motion Estimator and Correlation Filter

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 to derive a motion estimation model. It combines it with two DCF-based trackers, namely, the kernelized correlation filter and the discriminative correlation filter with channel and spatial reliability for pedestrian tracking. Camera motion-compensated 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, 4 GB RAM) board have been presented.