WignerMSER: Pseudo-Wigner Distribution Enriched MSER Feature Detector for Object Recognition in Thermal Infrared Images

In this work, we introduce WignerMSER, a robust local feature detector for thermal infrared images, a new variant of renowned Maximally Stable Extremal Regions (MSER) feature detector. Thermal Infrared images pose challenges of low contrast and lack of sharp edges or boundaries typically required for identifying keypoints using traditional feature detectors. This work addresses this challenge by designing a local feature detector suitable for infrared images. The proposed WignerMSER features are obtained by transforming the image from the spatial domain to joint space–spatial-frequency domain using pseudo Wigner-Ville Distribution, and thereafter detecting MSERs in the Wigner transformed space. Different variants of WignerMSER are proposed and their performance is compared over traditional MSER feature detector in a bag of words framework for the purpose of object recognition in thermal infrared images. The performance of WignerMSER feature detector is evaluated on two infrared datasets. The first dataset is CSIR-CSIO thermal infrared (TIR) vehicle dataset comprising of four vehicle categories commonly seen on urban roads in India and the second dataset is a subset of FLIR Thermal dataset comprising of two object categories, vehicles and pedestrians. All the variants of the proposed WignerMSER feature detector have demonstrated superior classification performance against traditional MSER features for object categorization on both the TIR datasets. Fused WignerMSER feature detector has shown the best performance with more than 5% increase in overall classification accuracy over traditional MSER for both the datasets.