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

Abstract

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 DCF-based trackers, namely Kernelized Correlation Filter (KCF) and Discriminative Correlation Filter with Channel and Spatial Reliability (CSRDCF) 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.