# Key-Point Sequence Lossless Compression for Intelligent Video Analysis

arXiv: https://arxiv.org/abs/2009.04646 · HTML: https://mlsanity.com/paper/2009.04646/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition](https://mlsanity.com/paper/2207.09759/) (2022)
- [Structured Attention Composition for Temporal Action Localization](https://mlsanity.com/paper/2205.09956/) (2022)
- [Few-shot Action Recognition via Intra- and Inter-Video Information Maximization](https://mlsanity.com/paper/2305.06114/) (2023)
- [Coding for Intelligence from the Perspective of Category](https://mlsanity.com/paper/2407.01017/) (2024)

## Abstract (arXiv)

Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and transmitted to the back-end for further processing. In this article, we present a lossless key-point sequence compression approach for efficient feature coding. The essence of this predict-and-encode strategy is to eliminate the spatial and temporal redundancies of key points in videos. Multiple prediction modes with an adaptive mode selection method are proposed to handle key-point sequences with various structures and motion. Experimental results validate the effectiveness of the proposed scheme on four types of widely used key-point sequences in video analysis.