# POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

arXiv: https://arxiv.org/abs/1610.06136 · HTML: https://mlsanity.com/paper/1610.06136/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2016.

## Built on by (most cited first, 30 of 74)

- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [Track to Detect and Segment: An Online Multi-Object Tracker](https://mlsanity.com/paper/2103.08808/) (2021)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [Monocular Quasi-Dense 3D Object Tracking](https://mlsanity.com/paper/2103.07351/) (2021)
- [Multiple Object Tracking with Correlation Learning](https://mlsanity.com/paper/2104.03541/) (2021)
- [Do Different Tracking Tasks Require Different Appearance Models?](https://mlsanity.com/paper/2107.02156/) (2021)
- [QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking](https://mlsanity.com/paper/2210.06984/) (2022)
- [RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation](https://mlsanity.com/paper/2105.04322/) (2021)
- [Simple Cues Lead to a Strong Multi-Object Tracker](https://mlsanity.com/paper/2206.04656/) (2022)
- [iKUN: Speak to Trackers without Retraining](https://mlsanity.com/paper/2312.16245/) (2023)
- [Unified Transformer Tracker for Object Tracking](https://mlsanity.com/paper/2203.15175/) (2022)
- [CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion](https://mlsanity.com/paper/2212.01247/) (2022)
- [One More Check: Making "Fake Background" Be Tracked Again](https://mlsanity.com/paper/2104.09441/) (2021)
- [Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation](https://mlsanity.com/paper/2302.14589/) (2023)
- [Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming](https://mlsanity.com/paper/2101.01060/) (2021)
- [Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model](https://mlsanity.com/paper/2105.01213/) (2021)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets](https://mlsanity.com/paper/2407.08872/) (2024)
- [Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment](https://mlsanity.com/paper/2304.09471/) (2023)
- [Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation](https://mlsanity.com/paper/2106.10950/) (2021)
- [Learning Data Association for Multi-Object Tracking using Only Coordinates](https://mlsanity.com/paper/2403.08018/) (2024)
- [FeatureSORT: Essential Features for Effective Tracking](https://mlsanity.com/paper/2407.04249/) (2024)
- [Few-Shot Video Object Detection](https://mlsanity.com/paper/2104.14805/) (2021)
- [Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking](https://mlsanity.com/paper/2405.18606/) (2024)
- [Real-time Online Multi-Object Tracking in Compressed Domain](https://mlsanity.com/paper/2204.02081/) (2022)
- [Towards Frame Rate Agnostic Multi-Object Tracking](https://mlsanity.com/paper/2209.11404/) (2022)
- [AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes](https://mlsanity.com/paper/2308.07537/) (2023)
- [Privacy-sensitive Objects Pixelation for Live Video Streaming](https://mlsanity.com/paper/2101.00604/) (2021)
- [Motion Vector Extrapolation for Video Object Detection](https://mlsanity.com/paper/2104.08918/) (2021)

## Abstract (arXiv)

Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available. In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version.