Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking (1609.06118v1)

Abstract: Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html .

• The paper presents a unified formulation that jointly optimizes the tracking model and sample quality weights to down-weight corrupted training samples.
• It employs an alternate convex search strategy to iteratively refine tracking accuracy, achieving a 3.8% improvement on OTB-2015.
• The approach enhances robustness in dynamic conditions and integrates seamlessly with state-of-the-art tracking systems.

Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking

In the domain of visual object tracking, maintaining the fidelity of training datasets is integral to the successful application of tracking-by-detection methods. The paper by Danelljan et al. introduces a methodology for adaptive decontamination of training sets within visual tracking systems. This approach addresses inefficiencies related to corrupted training samples, a persistent challenge exacerbated by misalignments, occlusions, and noise during the collection of samples.

The method presented by Danelljan et al. is rooted in the discerning estimation of sample quality. It proposes a unified formulation that integrates the minimization of a single loss function governing both the target appearance model and the corresponding sample quality weights. This joint solution equips the framework to down-weight compromised samples effectively, thus optimizing the balance between reliance on accurate vs. corrupted data.

The unified formulation extends beyond existing methodologies by eliminating the dependence on separate training sample management components, thereby integrating the evaluation of the sample weights directly within the learning process. This biconvex problem is articulated through an alternate convex search strategy, leveraging biconvexity to optimize both the tracking model and the sample weights iteratively.

Empirical validation of the approach across multiple visual tracking benchmarks—OTB-2015, VOT-2015, and Temple-Color—demonstrates its efficacy. The results indicate that this approach consistently improves the mean overlap precision to significant extents, outperforming preceding methods with a 3.8% enhancement on OTB-2015. This improvement substantiates the merit of the joint formulation for capturing the significance of temporally dynamic sample qualities.

From a theoretical perspective, this research broadens the scope of adaptive learning in real-time applications by redefining the conventionally discrete sample treatment to a continuous evaluation framework. This not only fortifies robustness against rapid changes in target appearances, environmental conditions, and occlusions but also ingrains resilience against model drift and ultimate failure.

Practically, the implication of this work is far-reaching. It seamlessly integrates with existing tracking systems without extensive modifications, demonstrating versatility and applicability across a range of discriminative models including SVM and correlation filters. Additionally, the computational demands of optimizing the sample weights are minimal, ensuring its applicability in time-constricted environments.

Looking forward, this paper lays foundational work that can be expanded in future studies. The incorporation of more sophisticated dynamic sample priors or integration within neural-based tracking models could potentially elevate the performance ceilings further. Moreover, the interaction of this unified formulation with unsupervised learning paradigms or domain adaptation techniques could present intriguing opportunities for future investigation.

In summary, the methodological innovation of this paper resides in its conceptual shift from static training set management to a dynamic, adaptive learning model. This not only augments tracking precision but also extends the adaptive capabilities of discriminative visual trackers amid real-world turbulence.