1 research outputs found
Tracking system of Mine Patrol Robot for Low Illumination Environment
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.Comment: 13 pages, 7 figures, 1 table, 27 conferenc