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BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking.
Summary
This study introduces hyperspectral camouflaged object tracking (HCOT) and a new dataset, BihoT, to address limitations in existing hyperspectral object tracking (HOT) methods. The proposed Spectral Prompt-based Distractor-Aware Network (SPDAN) achieves state-of-the-art performance on challenging camouflage scenarios.
Area of Science:
- Computer Vision
- Remote Sensing
- Machine Learning
Background:
- Hyperspectral object tracking (HOT) is crucial for identifying camouflaged objects.
- Existing HOT datasets often exhibit bias, favoring visual over spectral features, limiting tracker performance in challenging scenarios.
- This bias hinders trackers from effectively utilizing spectral information when visual cues are unreliable.
Purpose of the Study:
- Introduce a new task: hyperspectral camouflaged object tracking (HCOT).
- Develop a large-scale dataset (BihoT) specifically for HCOT, featuring diverse camouflage scenes, similar appearances, varied spectrums, and frequent occlusion.
- Propose a novel baseline model, Spectral Prompt-based Distractor-Aware Network (SPDAN), to address the challenges of HCOT.
Main Methods:
- Constructed the BihoT dataset with 41,912 hyperspectral images (HSIs) across 49 video sequences.
- Developed SPDAN, comprising a Spectral Embedding Network (SEN) for spectral-spatial feature extraction, a Spectral Prompt-based Backbone Network (SPBN) for fine-tuning RGB trackers with spectral prompts, and a Distractor-Aware Module (DAM) for handling occlusion and distractors.
- SEN utilizes 3-D and 2-D convolutions; SPBN leverages spectral prompts to mitigate training sample scarcity; DAM employs a novel statistic and motion predictor to correct performance degradation.
Main Results:
- SPDAN demonstrated state-of-the-art performance on the BihoT dataset.
- The proposed model also achieved superior results on other existing HOT datasets.
- The effectiveness of SPDAN in hyperspectral camouflaged object tracking was validated through extensive experiments.
Conclusions:
- The new HCOT task and BihoT dataset provide a robust benchmark for evaluating trackers in challenging camouflage scenarios.
- SPDAN offers an effective solution for hyperspectral object tracking, particularly when objects are camouflaged and visual features are unreliable.
- The spectral prompt-based approach and distractor-aware mechanism significantly improve tracking accuracy in complex environments.
