Enhanced Object Tracking Using Kalman Filters and Deep Features

Abstract

Object tracking plays a critical role in computer vision applications, including autonomous driving, surveillance, and human-computer interaction. Traditional tracking methods using Kalman Filters (KF) excel in predicting object motion but are sensitive to occlusions, appearance changes, and background clutter. Recent advances in deep learning have introduced robust feature extractors capable of capturing high-level semantic representations, which, when integrated with KF-based motion models, can significantly enhance tracking accuracy and robustness. This paper presents an enhanced object tracking framework that combines the predictive capabilities of Kalman Filters with discriminative deep features extracted from convolutional neural networks (CNNs).

The hybrid model addresses common challenges in real-world tracking scenarios such as abrupt motion, scale variations, and partial occlusions. Simulation experiments were conducted on benchmark datasets such as MOT16 and KITTI, demonstrating improvements in tracking precision, Multiple Object Tracking Accuracy (MOTA), and ID switch reduction compared to standalone KF and deep learning-based trackers. Statistical analysis reveals that the integration of deep features with KF yields an average 14.6% improvement in tracking accuracy across varied environmental conditions. The proposed approach shows promising potential for deployment in real-time intelligent vision systems.