Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

TL;DR

This paper presents a CNN-based method for detecting and tracking small moving objects in Wide Area Motion Imagery, combining background subtraction with neural networks to improve accuracy and reduce false alarms.

Contribution

It introduces a novel combination of background subtraction and CNNs for small object detection and tracking in WAMI, addressing false alarms and merged detections.

Findings

Competitive detection performance on small objects.

Effective false alarm rejection using CNNs.

Improved multi-object tracking with GM-PHD filter.

Abstract

This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated. Identifying the objects only using foreground appearance is difficult since a $100-$pixel vehicle is hard to distinguish from objects comprising the background. Our approach is based on background subtraction as an efficient and unsupervised method that is able to output the shape of objects. In order to reliably detect low contrast and small objects, we configure the background subtraction to extract foreground regions that might be objects of interest. While this dramatically increases the number of false alarms, a Convolutional Neural Network (CNN) considering both spatial and temporal information is then trained to reject the false alarms. In areas with heavy traffic, the background subtraction yields merged detections. To reduce the complexity of multi-target tracker needed, we train another CNN to predict the positions of multiple moving objects in an area. Our approach shows competitive detection performance on smaller objects relative to the state-of-the-art. We adopt a GM-PHD filter to associate detections over time and analyse the resulting performance.