# Semi-Automatic Annotation For Visual Object Tracking

arXiv: https://arxiv.org/abs/2101.06977 · HTML: https://mlsanity.com/paper/2101.06977/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Automatic adaptation of object detectors to new domains using self-training](https://mlsanity.com/paper/1904.07305/) (2019)
- [Unsupervised Hard Example Mining from Videos for Improved Object Detection](https://mlsanity.com/paper/1808.04285/) (2018)
- [We don't need no bounding-boxes: Training object class detectors using only human verification](https://mlsanity.com/paper/1602.08405/) (2016)
- [Iterative Bounding Box Annotation for Object Detection](https://mlsanity.com/paper/2007.00961/) (2020)
- [Faster Bounding Box Annotation for Object Detection in Indoor Scenes](https://mlsanity.com/paper/1807.03142/) (2018)
- [Efficient video annotation with visual interpolation and frame selection guidance](https://mlsanity.com/paper/2012.12554/) (2020)
- [Learning Intelligent Dialogs for Bounding Box Annotation](https://mlsanity.com/paper/1712.08087/) (2017)
- [Effect of Annotation Errors on Drone Detection with YOLOv3](https://mlsanity.com/paper/2004.01059/) (2020)

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

- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Objects do not disappear: Video object detection by single-frame object location anticipation](https://mlsanity.com/paper/2308.04770/) (2023)
- [Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID](https://mlsanity.com/paper/2503.17237/) (2025)
- [Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges](https://mlsanity.com/paper/2507.10006/) (2025)
- [Learning from Distributed Eyes: Leveraging Collaborative Perception for Automated Model Adaptation](https://mlsanity.com/paper/2609.18511/) (2026)
- [Iterative Learning for Instance Segmentation](https://mlsanity.com/paper/2202.09110/) (2022)
- [A Scale-Invariant Trajectory Simplification Method for Efficient Data Collection in Videos](https://mlsanity.com/paper/2304.01340/) (2023)
- [Automated Multimodal Data Annotation via Calibration With Indoor Positioning System](https://mlsanity.com/paper/2312.03608/) (2023)

## Abstract (arXiv)

We propose a semi-automatic bounding box annotation method for visual object tracking by utilizing temporal information with a tracking-by-detection approach. For detection, we use an off-the-shelf object detector which is trained iteratively with the annotations generated by the proposed method, and we perform object detection on each frame independently. We employ Multiple Hypothesis Tracking (MHT) to exploit temporal information and to reduce the number of false-positives which makes it possible to use lower objectness thresholds for detection to increase recall. The tracklets formed by MHT are evaluated by human operators to enlarge the training set. This novel incremental learning approach helps to perform annotation iteratively. The experiments performed on AUTH Multidrone Dataset reveal that the annotation workload can be reduced up to 96% by the proposed approach.