# An Image Processing Pipeline for Camera Trap Time-Lapse Recordings

arXiv: https://arxiv.org/abs/2206.05159 · HTML: https://mlsanity.com/paper/2206.05159/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer](https://mlsanity.com/paper/1902.09324/) (2019)

## Abstract (arXiv)

A new open-source image processing pipeline for analyzing camera trap time-lapse recordings is described. This pipeline includes machine learning models to assist human-in-the-loop video segmentation and animal re-identification. We present some performance results and observations on the utility of this pipeline after using it in a year-long project studying the spatial ecology and social behavior of the gopher tortoise.