# Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems

arXiv: https://arxiv.org/abs/2312.08213 · HTML: https://mlsanity.com/paper/2312.08213/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [Event-based Asynchronous Sparse Convolutional Networks](https://mlsanity.com/paper/2003.09148/) (2020)
- [Time-Ordered Recent Event (TORE) Volumes for Event Cameras](https://mlsanity.com/paper/2103.06108/) (2021)
- [Event Enhanced High-Quality Image Recovery](https://mlsanity.com/paper/2007.08336/) (2020)
- [Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras](https://mlsanity.com/paper/1805.07931/) (2018)
- [Video Super Resolution Based on Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2007.12928/) (2020)
- [E-CIR: Event-Enhanced Continuous Intensity Recovery](https://mlsanity.com/paper/2203.01935/) (2022)
- [DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos](https://mlsanity.com/paper/2203.03996/) (2022)
- [An Asynchronous Intensity Representation for Framed and Event Video Sources](https://mlsanity.com/paper/2301.08783/) (2023)

## Built on by (most cited first, 3 of 3)

- [An Open Software Suite for Event-Based Video](https://mlsanity.com/paper/2401.17151/) (2024)
- [adder-viz: Real-Time Visualization Software for Transcoding Event Video](https://mlsanity.com/paper/2508.14996/) (2025)
- [Scalable Event-Based Video Streaming for Machines with MoQ](https://mlsanity.com/paper/2508.15003/) (2025)

## Abstract (arXiv)

The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called ADDER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.