# Circulant temporal encoding for video retrieval and temporal alignment

arXiv: https://arxiv.org/abs/1506.02588 · HTML: https://mlsanity.com/paper/1506.02588/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2015.

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

- [The 2023 Video Similarity Dataset and Challenge](https://mlsanity.com/paper/2306.09489/) (2023)
- [Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval](https://mlsanity.com/paper/2309.11091/) (2023)
- [VADER: Video Alignment Differencing and Retrieval](https://mlsanity.com/paper/2303.13193/) (2023)
- [VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression](https://mlsanity.com/paper/2303.08906/) (2023)
- [Sync from the Sea: Retrieving Alignable Videos from Large-Scale Datasets](https://mlsanity.com/paper/2409.01445/) (2024)
- [AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications](https://mlsanity.com/paper/2501.10396/) (2024)
- [Query by Activity Video in the Wild](https://mlsanity.com/paper/2311.13895/) (2023)
- [SyncTrack4D: Cross-Video Motion Alignment and Video Synchronization for Multi-Video 4D Gaussian Splatting](https://mlsanity.com/paper/2512.04315/) (2025)

## Abstract (arXiv)

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene.