# POPCat: Propagation of particles for complex annotation tasks

arXiv: https://arxiv.org/abs/2406.17183 · HTML: https://mlsanity.com/paper/2406.17183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories](https://mlsanity.com/paper/2204.04153/) (2022)
- [Flow-Guided Feature Aggregation for Video Object Detection](https://mlsanity.com/paper/1703.10025/) (2017)
- [Detect to Track and Track to Detect](https://mlsanity.com/paper/1710.03958/) (2017)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video](https://mlsanity.com/paper/1702.00824/) (2017)
- [GlobalTrack: A Simple and Strong Baseline for Long-term Tracking](https://mlsanity.com/paper/1912.08531/) (2019)
- [Few-shot Object Counting and Detection](https://mlsanity.com/paper/2207.10988/) (2022)
- [AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild](https://mlsanity.com/paper/2205.00158/) (2022)
- [GMOT-40: A Benchmark for Generic Multiple Object Tracking](https://mlsanity.com/paper/2011.11858/) (2020)
- [Siamese-DETR for Generic Multi-Object Tracking](https://mlsanity.com/paper/2310.17875/) (2023)
- [Z-GMOT: Zero-shot Generic Multiple Object Tracking](https://mlsanity.com/paper/2305.17648/) (2023)
- [Fully-Automated Packaging Structure Recognition in Logistics Environments](https://mlsanity.com/paper/2008.04620/) (2020)
- [Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach](https://mlsanity.com/paper/2304.00950/) (2023)

## Abstract (arXiv)

Novel dataset creation for all multi-object tracking, crowd-counting, and industrial-based videos is arduous and time-consuming when faced with a unique class that densely populates a video sequence. We propose a time efficient method called POPCat that exploits the multi-target and temporal features of video data to produce a semi-supervised pipeline for segmentation or box-based video annotation. The method retains the accuracy level associated with human level annotation while generating a large volume of semi-supervised annotations for greater generalization. The method capitalizes on temporal features through the use of a particle tracker to expand the domain of human-provided target points. This is done through the use of a particle tracker to reassociate the initial points to a set of images that follow the labeled frame. A YOLO model is then trained with this generated data, and then rapidly infers on the target video. Evaluations are conducted on GMOT-40, AnimalTrack, and Visdrone-2019 benchmarks. These multi-target video tracking/detection sets contain multiple similar-looking targets, camera movements, and other features that would commonly be seen in "wild" situations. We specifically choose these difficult datasets to demonstrate the efficacy of the pipeline and for comparison purposes. The method applied on GMOT-40, AnimalTrack, and Visdrone shows a margin of improvement on recall/mAP50/mAP over the best results by a value of 24.5%/9.6%/4.8%, -/43.1%/27.8%, and 7.5%/9.4%/7.5% where metrics were collected.