CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking (2505.05936v1)

Abstract: Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotics applications. While the integration of hierarchical lightweight networks has become a prevalent strategy for enhancing efficiency in UAV tracking, it often results in a significant drop in network capacity, which further exacerbates challenges in UAV scenarios, such as frequent occlusions and extreme changes in viewing angles. To address these issues, we introduce a novel family of UAV trackers, termed CGTrack, which combines explicit and implicit techniques to expand network capacity within a coarse-to-fine framework. Specifically, we first introduce a Hierarchical Feature Cascade (HFC) module that leverages the spirit of feature reuse to increase network capacity by integrating the deep semantic cues with the rich spatial information, incurring minimal computational costs while enhancing feature representation. Based on this, we design a novel Lightweight Gated Center Head (LGCH) that utilizes gating mechanisms to decouple target-oriented coordinates from previously expanded features, which contain dense local discriminative information. Extensive experiments on three challenging UAV tracking benchmarks demonstrate that CGTrack achieves state-of-the-art performance while running fast. Code will be available at https://github.com/Nightwatch-Fox11/CGTrack.

Overview of CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking

The paper discusses the development of CGTrack, a novel tracking architecture aimed at enhancing the efficiency and robustness of unmanned aerial vehicle (UAV) tracking. UAV tracking is pivotal for various applications in robotics and automation, particularly given the challenges posed by UAV dynamics such as occlusions and rapid changes in viewing angles. CGTrack introduces a unique approach by integrating hierarchical feature aggregation and cascade gating within a lightweight framework.

Key Contributions

1. Hierarchical Feature Cascade (HFC) Module:
   • The HFC module is inspired by the DenseNet architecture, leveraging feature reuse through a cascade of features from different network layers. It successfully increases network capacity without significantly adding to computational costs. This module consolidates deep semantic cues with spatial information, enhancing the feature representation crucial for effective UAV tracking.
2. Lightweight Gated Center Head (LGCH):
   • The LGCH utilizes a novel gating mechanism that segregates target-oriented coordinates from the enriched features. This decoupling is significant in identifying and isolating dense, discriminative information, which is critical in UAV tracking scenarios that involve rapid and unpredictable target movements.

Methodology

CGTrack is grounded on the premise of maximizing network efficiency while retaining the network’s ability to handle complex spatial features inherent in UAV tracking. The approach is centered around a hierarchical vision transformer (ViT) architecture that is lightweight but capable of handling multidimensional data efficiently.

• Hierarchical Feature Aggregation: Instead of following traditional hierarchical feature fusion, which can result in insufficient discriminative power, CGTrack employs a Hierarchical Feature Cascade structure to preserve and enhance the network’s feature discriminability by explicitly mapping and gating features.
• Gating Mechanisms: The proposed LGCH incorporates gating effectively to map features into a high-dimensional space through efficient computing operations such as the Hadamard product. This mechanism enhances the network’s capability to capture fine-grained details necessary for robust UAV tracking.

Experimental Validation

The efficacy of CGTrack is validated through extensive experiments on several UAV tracking benchmarks, including UAV123, UAV123@10fps, and UAVTrack112. The results underscore CGTrack’s superior performance, achieving state-of-the-art precision and success rates across these challenging datasets.

• Quantitative Results: CGTrack demonstrated higher precision scores and robustness compared to 13 state-of-the-art alternatives, with 6.6% and 8.4% performance gains over the average on the UAV123 benchmark.
• Efficiency: Despite the complexities involved in UAV tracking scenarios, CGTrack retains its efficiency, making it suitable for real-time applications on edge devices with constrained resources.

Theoretical Implications

The integration of hierarchical aggregation with gating in CGTrack provides a new dimension for developing adaptive and resource-efficient tracking networks. This architecture challenges the traditional trade-off between model complexity and computational efficiency, presenting a scalable solution applicable to various UAV tracking environments.

Future Directions

The paper hints at future developments that could further consolidate CGTrack’s application across other domains in real-world robotics and automation. Potential areas for exploration include scaling the architecture for larger object tracking datasets and exploring further improvements in gating mechanisms to enhance the discriminative power of lightweight networks.

In conclusion, CGTrack embodies a significant step forward in UAV tracking, combining lightweight design with advanced feature aggregation techniques to deliver fast, precise, and robust tracking performances. Its implications extend across both practical UAV applications and the fundamental understanding of hierarchical feature processing in AI architectures.