Towards Building a Real Time Mobile Device Bird Counting System Through Synthetic Data Training and Model Compression

TL;DR

This paper presents a real-time bird counting system using synthetic data training and model compression, achieving high accuracy and efficiency for deployment on mobile devices.

Contribution

It introduces a synthetic data generation method for bird counting and applies model compression to enable real-time mobile deployment.

Findings

Achieved approximately 12.4 MSE on real bird dataset

Reduced model size from 55MB to under 5MB with minimal accuracy loss

Demonstrated effective density map estimation for bird counting

Abstract

Counting the number of birds in an open sky setting has been an challenging problem due to the large number of bird flocks and the birds can overlap. Another difficulty is the lack of accurate training samples since the cost of labeling images of bird flocks can be extremely high and each sample picture can contain thousands of birds in a high resolution image. Inspired by recent work on training with synthetic data to perform crowd counting, we design a mechanism to generate synthetic bird dataset with precise bird count and the corresponding density maps. We then train a Unet model on the synthetic dataset to perform density map estimation that produces the count for each input. Our method is able to achieve MSE of approximately 12.4 on real dataset. In order to build a scalable system for fast bird counting under storage and computational constraints, we use model compression techniques and efficient model structures to increase the inference speed and save storage cost. We are able to reduce storage cost from 55MB to less than 5MB for the model with minimum loss of accuracy. This paper describes the pipelines of building an efficient bird counting system.