# Exploring Enhanced Contextual Information for Video-Level Object Tracking

arXiv: https://arxiv.org/abs/2412.11023 · HTML: https://mlsanity.com/paper/2412.11023/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 41 of its 56 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 30 of 41)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Ocean: Object-aware Anchor-free Tracking](https://mlsanity.com/paper/2006.10721/) (2020)
- [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020)
- [SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines](https://mlsanity.com/paper/1911.06188/) (2019)
- [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [AiATrack: Attention in Attention for Transformer Visual Tracking](https://mlsanity.com/paper/2207.09603/) (2022)
- [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://mlsanity.com/paper/2009.03465/) (2020)
- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [ODTrack: Online Dense Temporal Token Learning for Visual Tracking](https://mlsanity.com/paper/2401.01686/) (2024)
- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)

## Built on by (most cited first, 30 of 37)

- [What You Have is What You Track: Adaptive and Robust Multimodal Tracking](https://mlsanity.com/paper/2507.05899/) (2025)
- [AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding](https://mlsanity.com/paper/2507.02591/) (2025)
- [HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking](https://mlsanity.com/paper/2507.07603/) (2025)
- [FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking](https://mlsanity.com/paper/2504.13604/) (2025)
- [Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking](https://mlsanity.com/paper/2506.20381/) (2025)
- [Advances in Artificial Intelligence: A Review for the Creative Industries](https://mlsanity.com/paper/2501.02725/) (2025)
- [Hyperspectral Mamba for Hyperspectral Object Tracking](https://mlsanity.com/paper/2509.08265/) (2025)
- [UETrack: A Unified and Efficient Framework for Single Object Tracking](https://mlsanity.com/paper/2603.01412/) (2026)
- [MVTD: A Benchmark Dataset for Maritime Visual Object Tracking](https://mlsanity.com/paper/2506.02866/) (2025)
- [Learning Frequency and Memory-Aware Prompts for Multi-Modal Object Tracking](https://mlsanity.com/paper/2506.23972/) (2025)
- [DMTrack: Spatio-Temporal Multimodal Tracking via Dual-Adapter](https://mlsanity.com/paper/2508.01592/) (2025)
- [Multi-State Tracker: Enhancing Efficient Object Tracking via Multi-State Specialization and Interaction](https://mlsanity.com/paper/2508.11531/) (2025)
- [Tracking and Segmenting Anything in Any Modality](https://mlsanity.com/paper/2511.19475/) (2025)
- [How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2512.07385/) (2025)
- [Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking](https://mlsanity.com/paper/2601.01022/) (2026)
- [GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing](https://mlsanity.com/paper/2602.08550/) (2026)
- [SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement](https://mlsanity.com/paper/2602.20636/) (2026)
- [Uni-MDTrack: Learning Decoupled Memory and Dynamic States for Parameter-Efficient Visual Tracking in All Modality](https://mlsanity.com/paper/2603.14452/) (2026)
- [Drift-Resilient Temporal Priors for Visual Tracking](https://mlsanity.com/paper/2604.02654/) (2026)
- [Unified Multimodal Visual Tracking with Dual Mixture-of-Experts](https://mlsanity.com/paper/2605.03716/) (2026)
- [Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning](https://mlsanity.com/paper/2605.06092/) (2026)
- [An Efficient Token Compression Framework for Visual Object Tracking](https://mlsanity.com/paper/2605.08329/) (2026)
- [Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking](https://mlsanity.com/paper/2607.26511/) (2026)
- [DPTrack:Directional Kernel-Guided Prompt Learning for Robust Nighttime Aerial Tracking](https://mlsanity.com/paper/2510.15449/) (2025)
- [MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations](https://mlsanity.com/paper/2510.21586/) (2025)
- [Exploring Modality-Aware Fusion and Decoupled Temporal Propagation for Multi-Modal Object Tracking](https://mlsanity.com/paper/2603.09287/) (2026)
- [Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation](https://mlsanity.com/paper/2604.01974/) (2026)
- [Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking](https://mlsanity.com/paper/2605.06112/) (2026)
- [Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating](https://mlsanity.com/paper/2606.09167/) (2026)
- [Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking](https://mlsanity.com/paper/2606.26455/) (2026)

## Abstract (arXiv)

Contextual information at the video level has become increasingly crucial for visual object tracking. However, existing methods typically use only a few tokens to convey this information, which can lead to information loss and limit their ability to fully capture the context. To address this issue, we propose a new video-level visual object tracking framework called MCITrack. It leverages Mamba's hidden states to continuously record and transmit extensive contextual information throughout the video stream, resulting in more robust object tracking. The core component of MCITrack is the Contextual Information Fusion module, which consists of the mamba layer and the cross-attention layer. The mamba layer stores historical contextual information, while the cross-attention layer integrates this information into the current visual features of each backbone block. This module enhances the model's ability to capture and utilize contextual information at multiple levels through deep integration with the backbone. Experiments demonstrate that MCITrack achieves competitive performance across numerous benchmarks. For instance, it gets 76.6% AUC on LaSOT and 80.0% AO on GOT-10k, establishing a new state-of-the-art performance. Code and models are available at https://github.com/kangben258/MCITrack.