GlobalTrack: A Simple and Strong Baseline for Long-term Tracking (1912.08531v1)

Abstract: A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area. Code is available at https://github.com/huanglianghua/GlobalTrack.

• The paper introduces GlobalTrack, a baseline long-term tracker that performs a comprehensive global search to overcome the limitations of traditional temporal consistency assumptions.
• The methodology employs a Query-Guided RPN and RCNN to extract and correlate features, significantly improving recall and precision on benchmarks like LaSOT and TLP.
• Empirical evaluations demonstrate substantial performance gains, positioning GlobalTrack as a promising reference baseline for future long-term tracking research.

An Expert Assessment of "GlobalTrack: A Simple and Strong Baseline for Long-term Tracking"

The paper "GlobalTrack: A Simple and Strong Baseline for Long-term Tracking" introduces a novel approach to visual object tracking that circumvents common limitations in existing methods. Notably, the authors present GlobalTrack, a tracker developed from two-stage object detectors, which eliminates the underlying assumptions about the temporal consistency of target positions and scales found in many state-of-the-art trackers. This methodology allows GlobalTrack to perform a global search for arbitrary instances across an entire image with a robustness that stands up against abrupt target movements or absences. This paper provides numerical results that demonstrate the strength of the approach across several large-scale benchmarks, revealing its potential as a baseline for future research in long-term tracking.

Core Contributions and Methodology

GlobalTrack distinguishes itself by its reliance on a pure global instance search strategy, enabling extensive searching capabilities without assuming smooth transitions of target positions across frames. Existing trackers, including ATOM and SiamRPN++, generally incorporate a temporal consistency constraint, assuming minimal changes in target positioning between consecutive frames. This assumption results in failures under scenarios of rapid motion or target disappearance, a gap GlobalTrack aims to bridge.

The architecture of GlobalTrack leverages two core components: the Query-Guided Region Proposal Network (QG-RPN) and the Query-Guided Region Convolutional Neural Network (QG-RCNN). These components facilitate the extraction and correlation of query-specific features with search image features, ultimately guiding the network to identify query-specific instances. QG-RPN's robust architecture outperforms standard RPNs in generating high-recall proposals, while QG-RCNN offers enhancements in precision accuracy at low proposal counts. By synthesizing these with a cross-query loss function, the authors aim for heightened discriminative power against distractors, enriching the model's utility in complex visual scenes.

Empirical Evaluation

The authors showcase GlobalTrack's abilities through extensive experimentation and comparisons on multiple benchmarking datasets, including LaSOT, TrackingNet, TLP, and OxUvA. For instance, on the LaSOT benchmark, GlobalTrack achieves an AUC of 52.1%, which outperforms preceding state-of-the-art trackers by a noticeable margin. On the TLP benchmark, GlobalTrack sees a significant leap with an 11.1% absolute gain over SPLT in success rates. These results underscore the method's robustness against the typical pitfalls seen in long-term tracking tasks, such as cumulative errors over extended periods of absent targets or episodes of aggressive movement.

Moreover, GlobalTrack is highlighted for its ability to recover from temporary failings without detriment to subsequent performance, a critical aspect lacking in many existing approaches. For example, as observed on OxUvA, GlobalTrack's independence from cumulative error enhances its performance significantly with respect to both true positive and true negative rate scores.

Impact and Future Directions

The innovative reframing of long-term tracking articulated in this paper posits several implications. Practically, the method can be applied vividly across scenarios requiring persistent tracking without input updates over time, such as long-form surveillance or autonomous navigation systems. Theoretically, it positions a concrete baseline against which future tracking models can be measured, particularly highlighting the importance of reducing reliance on temporal consistency.

The authors express their intention for GlobalTrack to stimulate further research and exploration in the domain. Future development could investigate incorporating additional post-processing steps or modifying the current structure to embrace adaptive learning mechanisms while maintaining its simplistic execution flow.

In summary, "GlobalTrack" makes noteworthy strides in addressing the challenges associated with long-term visual tracking. Its contributions promise to impact subsequent research and practical applications significantly. With its open access code, the authors invite further examination and enhancement within the computer vision research community.