Underwater Camouflaged Object Tracking Meets Vision-Language SAM2 (2409.16902v5)

Abstract: Over the past decade, significant progress has been made in visual object tracking, largely due to the availability of large-scale datasets. However, these datasets have primarily focused on open-air scenarios and have largely overlooked underwater animal tracking-especially the complex challenges posed by camouflaged marine animals. To bridge this gap, we take a step forward by proposing the first large-scale multi-modal underwater camouflaged object tracking dataset, namely UW-COT220. Based on the proposed dataset, this work first comprehensively evaluates current advanced visual object tracking methods, including SAM- and SAM2-based trackers, in challenging underwater environments, \eg, coral reefs. Our findings highlight the improvements of SAM2 over SAM, demonstrating its enhanced ability to handle the complexities of underwater camouflaged objects. Furthermore, we propose a novel vision-language tracking framework called VL-SAM2, based on the video foundation model SAM2. Extensive experimental results demonstrate that the proposed VL-SAM2 achieves state-of-the-art performance across underwater and open-air object tracking datasets. The dataset and codes are available at~{\color{magenta}{https://github.com/983632847/Awesome-Multimodal-Object-Tracking}}.

• The paper introduces UW-COT, the first large-scale dataset for underwater camouflaged object tracking, addressing domain-specific challenges.
• The paper demonstrates that SAM 2 outperforms prior methods with improved temporal consistency, occlusion handling, and feature embedding.
• The study sets a new benchmark for underwater tracking, offering insights on balancing model size and computational efficiency in challenging conditions.

Towards Underwater Camouflaged Object Tracking: An Experimental Evaluation of SAM and SAM 2

The paper "Towards Underwater Camouflaged Object Tracking: An Experimental Evaluation of SAM and SAM 2" by Chunhui Zhang et al. makes considerable strides in addressing the challenges inherent in visual object tracking (VOT) within underwater environments. Propelled by the scarcity of specialized datasets for the underwater domain, the authors put forth UW-COT, the first large-scale dataset for tracking camouflaged objects in such environments.

Introduction and Background

Visual object tracking entails locating a target object throughout a video sequence, a task fundamental in applications ranging from autonomous vehicles and surveillance to robotics. Traditional VOT techniques, bolstered by large-scale open-air datasets, have seen significant advancements. Nevertheless, their efficacy diminishes in underwater scenarios due to factors like visual camouflage and light scattering. Whereas existing methods predominantly address open-air conditions, the need for robust underwater tracking mechanisms has driven the current paper.

Historical efforts in VOT have gravitated toward several methodologies: correlation filter-based methods, Siamese-based networks, and more recent Transformer-based and Mamba-based approaches. Specifically, foundational segmentation models such as SAM and SAM 2 have garnered attention for their applicability in challenging environments by leveraging advanced segmentation techniques.

Contribution and Dataset Composition

The UW-COT dataset is a pivotal contribution of this work, comprising 220 video sequences spanning 96 categories and approximately 159,000 frames. Each sequence includes bounding box and pseudo mask annotations for camouflaged objects, enhancing precision in object identification and tracking. For comparative analysis, the dataset's scale and diversity starkly surpass those of existing datasets like CAD, MoCA-Mask, and COTD.

Methodology and Experimental Setup

The paper evaluates several state-of-the-art (SOTA) VOT methods, notably including SAM, SAM-DA, Tracking Anything, and more advanced SAM 2. Comparative analysis also extends to contemporary models such as OSTrack, SeqTrack, and ARTrack. Metrics for evaluation encompass AUC (Area Under Curve), normalized precision (nPre), precision (Pre), complete AUC (cAUC), and mean intersection-over-union accuracy (mACC).

Experimental Results

The results elucidate SAM 2's superior capability in handling underwater camouflaged object tracking compared to its predecessors and other advanced VOT methods. The significant performance leap stems from SAM 2's enhancements in temporal consistency, robustness to occlusions, feature embedding, computational efficiency, motion estimation, domain generalization, and contextual integration. As per detailed evaluation:

• Center Point Prompt Efficacy: Center point prompts for SAM 2 consistently outperform random point prompts, underscoring the significance of prompt quality in interactive segmentation models.
• Model Size: Larger models generally demonstrate better performance at the expense of speed, revealing a performance trade-off inherent in model scaling.

Implications and Future Research

The implications of this research extend both practically and theoretically. UW-COT sets a new benchmark, providing a rich resource for advancing tracking technologies tailored for underwater environments. SAM 2's outperformance accentuates the potential for leveraging advanced segmentation models to resolve dynamic tracking challenges inherent in video data.

Future research directions could include expanding the scale and diversity of UW-COT to encompass more categories and underwater conditions, and investigating multi-modal approaches for underwater vision tasks. Additionally, addressing the balance between model complexity and computational efficiency remains a promising avenue for further exploration.

This paper not only augments the VOT landscape for underrepresented domains but also catalyzes further inquiry into specialized tracking methodologies essential for diverse application areas. Overall, the paper marks a significant step in refining underwater object tracking technologies, with SAM 2 exemplifying the strides possible through dedicated research and innovative dataset construction.