# SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences

arXiv: https://arxiv.org/abs/2112.14934 · HTML: https://mlsanity.com/paper/2112.14934/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2107.08373/) (2021)
- [Towards Image Understanding from Deep Compression without Decoding](https://mlsanity.com/paper/1803.06131/) (2018)
- [Deep feature compression for collaborative object detection](https://mlsanity.com/paper/1802.03931/) (2018)
- [High efficiency compression for object detection](https://mlsanity.com/paper/1710.11151/) (2017)
- [Multi-task learning with compressible features for Collaborative Intelligence](https://mlsanity.com/paper/1902.05179/) (2019)
- [Latent-space scalability for multi-task collaborative intelligence](https://mlsanity.com/paper/2105.10089/) (2021)
- [MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection](https://mlsanity.com/paper/1805.00107/) (2018)
- [Can you tell a face from a HEVC bitstream?](https://mlsanity.com/paper/1709.02993/) (2017)
- [Can you find a face in a HEVC bitstream?](https://mlsanity.com/paper/1710.10736/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Coding Framework and Benchmark towards Low-Bitrate Video Understanding](https://mlsanity.com/paper/2202.02813/) (2022)
- [Does Video Compression Impact Tracking Accuracy?](https://mlsanity.com/paper/2202.00892/) (2022)

## Abstract (arXiv)

We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. Ground-truth annotations for 13 sequences were prepared and released as the dataset called SFU-HW-Tracks-v1. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking.