Drift-Resilient Temporal Priors for Visual Tracking

2026-04-02

AI Summary

This work addresses the issue of model drift in multi-frame visual tracking, which commonly arises from naively aggregating noisy historical predictions. To mitigate this, the authors propose DTPTrack, a lightweight and general-purpose module comprising a Temporal Reliability Calibrator (TRC) and a Temporal Guidance Synthesizer (TGS). The TRC dynamically evaluates the reliability of past tracking states, while the TGS leverages these assessments to generate high-quality temporal priors that guide the tracker robustly against drift. Designed for seamless integration, DTPTrack is readily compatible with mainstream architectures such as OSTrack, ODTrack, and LoRAT. Extensive experiments demonstrate its effectiveness, achieving a 77.5% Success rate on LaSOT and an 80.3% AO on GOT-10k, thereby significantly enhancing tracking robustness and setting new state-of-the-art results across multiple benchmarks.

Abstract

Temporal information is crucial for visual tracking, but existing multi-frame trackers are vulnerable to model drift caused by naively aggregating noisy historical predictions. In this paper, we introduce DTPTrack, a lightweight and generalizable module designed to be seamlessly integrated into existing trackers to suppress drift. Our framework consists of two core components: (1) a Temporal Reliability Calibrator (TRC) mechanism that learns to assign a per-frame reliability score to historical states, filtering out noise while anchoring on the ground-truth template; and (2) a Temporal Guidance Synthesizer (TGS) module that synthesizes this calibrated history into a compact set of dynamic temporal priors to provide predictive guidance. To demonstrate its versatility, we integrate DTPTrack into three diverse tracking architectures--OSTrack, ODTrack, and LoRAT-and show consistent, significant performance gains across all baselines. Our best-performing model, built upon an extended LoRATv2 backbone, sets a new state-of-the-art on several benchmarks, achieving a 77.5% Success rate on LaSOT and an 80.3% AO on GOT-10k.

Problem

• visual tracking
• model drift
• temporal information
• noisy predictions

Innovation

• Temporal Priors
• Model Drift
• Visual Tracking
• Reliability Calibration
• Multi-frame Tracking