Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change

by   Taeyeong Choi, et al.

Biology is both an important application area and a source of motivation for development of advanced machine learning techniques. Although much attention has been paid to large and complex data sets resulting from high-throughput sequencing, advances in high-quality video recording technology have begun to generate similarly rich data sets requiring sophisticated techniques from both computer vision and time-series analysis. Moreover, just as studying gene expression patterns in one organism can reveal general principles that apply to other organisms, the study of complex social interactions in an experimentally tractable model system, such as a laboratory ant colony, can provide general principles about the dynamics many other social groups. Here, we focus on one such example from the study of reproductive regulation in small laboratory colonies of ∼50 Harpgenathos ants. These ants can be artificially induced to begin a ∼20 day process of hierarchy reformation. Although the conclusion of this process is conspicuous to a human observer, it is still unclear which behaviors during the transients are contributing to the process. To address this issue, we explore the potential application of One-class Classification (OC) to the detection of abnormal states in ant colonies for which behavioral data is only available for the normal societal conditions during training. Specifically, we build upon the Deep Support Vector Data Description (DSVDD) and introduce the Inner-Outlier Generator (IO-GEN) that synthesizes fake "inner outlier" observations during training that are near the center of the DSVDD data description. We show that IO-GEN increases the reliability of the final OC classifier relative to other DSVDD baselines. This method can be used to screen video frames for which additional human observation is needed.


Outlier Detection by Consistent Data Selection Method

Often the challenge associated with tasks like fraud and spam detection[...

Unmasking the abnormal events in video

We propose a novel framework for abnormal event detection in video that ...

A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video

Abnormal event detection in video is a complex computer vision problem t...

Sampling Method for Fast Training of Support Vector Data Description

Support Vector Data Description (SVDD) is a popular outlier detection te...

Road User Abnormal Trajectory Detection using a Deep Autoencoder

In this paper, we focus on the development of a method that detects abno...

Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms

Most deep-learning frameworks for understanding biological swarms are de...

Please sign up or login with your details

Forgot password? Click here to reset