# Learning regression and verification networks for long-term visual tracking

arXiv: https://arxiv.org/abs/1809.04320 · HTML: https://mlsanity.com/paper/1809.04320/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking](https://mlsanity.com/paper/2103.16086/) (2021)
- [Global Tracking via Ensemble of Local Trackers](https://mlsanity.com/paper/2203.16092/) (2022)
- [Tracking by Joint Local and Global Search: A Target-aware Attention based Approach](https://mlsanity.com/paper/2106.04840/) (2021)
- [Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search](https://mlsanity.com/paper/2205.09676/) (2022)
- [TDIOT: Target-driven Inference for Deep Video Object Tracking](https://mlsanity.com/paper/2103.11017/) (2021)
- [Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network](https://mlsanity.com/paper/2103.15105/) (2021)

## Abstract (arXiv)

Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent. Until now, few attempts have been done although this task is much closer to designing practical tracking systems. In this work, we propose a novel long-term tracking framework based on deep regression and verification networks. The offline-trained regression model is designed using the object-aware feature fusion and region proposal networks to generate a series of candidates and estimate their similarity scores effectively. The verification network evaluates these candidates to output the optimal one as the tracked object with its classification score, which is online updated to adapt to the appearance variations based on newly reliable observations. The similarity and classification scores are combined to obtain a final confidence value, based on which our tracker can determine the absence of the target accurately and conduct image-wide re-detection to capture the target successfully when it reappears. Extensive experiments show that our tracker achieves the best performance on the VOT2018 long-term challenge and state-of-the-art results on the OxUvA long-term dataset.