Multimodal and Multi-task Audio-Visual Vehicle Detection and Classification

Abstract

Moving vehicle detection and classification using multimodal data is a challenging task in data collection, audio-visual alignment, and feature selection, and effective vehicle classification in uncontrolled environments. In this work, we first present a systematic way to align the multimodal data based the multimodal temporal panorama generation. Then various types of features are extracted to represent diverse and multimodal information. Those include global geometric features (aspect ratios, profiles), local structure features (HOGs), various audio features in both spectral and perceptual representations. A flexible sequential forward selection algorithm with multi-branch searching is used to select a set of important features at different levels of feature combinations. Finally, using the same datasets for two different classification tasks, we show that the roles of audio and visual features are task-specific. Furthermore, in both cases, the combination of some of the features with multimodal and complementary information can improve the accuracy than using the individual features only. Therefore finer and more accurate classification can be achieved by two different levels of integration: feature level and the decision level.