# Predictive Visual Tracking: A New Benchmark and Baseline Approach

arXiv: https://arxiv.org/abs/2103.04508 · HTML: https://mlsanity.com/paper/2103.04508/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [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)
- [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)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Learning Background-Aware Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1703.04590/) (2017)
- [AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization](https://mlsanity.com/paper/2003.12949/) (2020)
- [Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking](https://mlsanity.com/paper/1908.02231/) (2019)
- [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](https://mlsanity.com/paper/1801.05944/) (2018)
- [CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark](https://mlsanity.com/paper/1907.00618/) (2019)
- [All-Day Object Tracking for Unmanned Aerial Vehicle](https://mlsanity.com/paper/2101.08446/) (2021)
- [Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments](https://mlsanity.com/paper/1904.02319/) (2019)
- [Real-Time Correlation Tracking via Joint Model Compression and Transfer](https://mlsanity.com/paper/1907.09831/) (2019)
- [Training-Set Distillation for Real-Time UAV Object Tracking](https://mlsanity.com/paper/2003.05326/) (2020)
- [Keyfilter-Aware Real-Time UAV Object Tracking](https://mlsanity.com/paper/2003.05218/) (2020)
- [Augmented Memory for Correlation Filters in Real-Time UAV Tracking](https://mlsanity.com/paper/1909.10989/) (2019)
- [Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters](https://mlsanity.com/paper/2008.00528/) (2020)
- [DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression](https://mlsanity.com/paper/2008.03912/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Real-World Visual Tracking with Temporal Contexts](https://mlsanity.com/paper/2308.10330/) (2023)
- [Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis](https://mlsanity.com/paper/2205.04281/) (2022)
- [PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework](https://mlsanity.com/paper/2211.11629/) (2022)
- [Towards Latency-Aware 3D Streaming Perception for Autonomous Driving](https://mlsanity.com/paper/2504.19115/) (2025)
- [Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation](https://mlsanity.com/paper/2112.09775/) (2021)

## Abstract (arXiv)

As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy and efficiency. Moreover, a new predictive visual tracking baseline is developed to compensate for the latency stemming from the onboard computation. Our latency-aware benchmark can provide a more realistic evaluation of the trackers for the robotic applications. Besides, exhaustive experiments have proven the effectiveness of the proposed predictive visual tracking baseline approach.