# Fast and Efficient Model for Real-Time Tiger Detection In The Wild

arXiv: https://arxiv.org/abs/1909.01122 · HTML: https://mlsanity.com/paper/1909.01122/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [An Efficient Illumination Invariant Tiger Detection Framework for Wildlife Surveillance](https://mlsanity.com/paper/2311.17552/) (2023)

## Abstract (arXiv)

The highest accuracy object detectors to date are based either on a two-stage approach such as Fast R-CNN or one-stage detectors such as Retina-Net or SSD with deep and complex backbones. In this paper we present TigerNet - simple yet efficient FPN based network architecture for Amur Tiger Detection in the wild. The model has 600k parameters, requires 0.071 GFLOPs per image and can run on the edge devices (smart cameras) in near real time. In addition, we introduce a two-stage semi-supervised learning via pseudo-labelling learning approach to distill the knowledge from the larger networks. For ATRW-ICCV 2019 tiger detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods.