Tracking system of Mine Patrol Robot for Low Illumination Environment

TL;DR

This paper introduces LLCT, a novel low-illumination long-term correlation tracker for robots, which improves tracking accuracy and efficiency in dark environments through fused features, PCA acceleration, and template management, achieving real-time performance.

Contribution

The paper presents a new tracker specifically designed for low-light conditions, combining fused features, PCA-based speed-up, and long-term memory for improved robustness and efficiency.

Findings

Outperforms state-of-the-art trackers in low-illumination environments

Achieves real-time tracking at 33 FPS

Demonstrates robustness and efficiency in robot systems

Abstract

Computer vision has received a significant attention in recent years, which is one of the important parts for robots to apperceive external environment. Discriminative Correlation Filter (DCF) based trackers gained more popularity due to their efficiency, however, tracking in low-illumination environments is a challenging problem, not yet successfully addressed in the literature. In this work, we tackle the problems by introducing Low-Illumination Long-term Correlation Tracker (LLCT). First, fused features only including HOG and Color Names are employed to boost the tracking efficiency. Second, we used the standard PCA to reduction scheme in the translation and scale estimation phase for accelerating. Third, we learned a long-term correlation filter to keep the long-term memory ability. Finally, update memory templates with interval updates, then re-match existing and initial templates every few frames to maintain template accuracy. The extensive experiments on popular Object Tracking Benchmark OTB-50 datasets have demonstrated that the proposed tracker outperforms the state-of-the-art trackers significantly achieves a high real-time (33FPS) performance. In addition, the proposed approach can be integrated easily in robot system and the running speed performed well. The experimental results show that the novel tracker performance in low-illumination environment is better than that of general trackers.