There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

TL;DR

This paper introduces a self-supervised multimodal framework that leverages RGB, depth, thermal, and audio data to improve multi-object detection and tracking, even in dynamic scenes, surpassing existing methods.

Contribution

The work presents a novel self-supervised distillation framework with a new MTA loss and a pretext task, enabling robust multi-object detection and tracking using sound across multiple modalities.

Findings

Outperforms state-of-the-art methods in multimodal object detection and tracking.

Effective in dynamic scenes with moving cameras and multiple objects.

Utilizes a large-scale multimodal dataset for training and evaluation.

Abstract

Attributes of sound inherent to objects can provide valuable cues to learn rich representations for object detection and tracking. Furthermore, the co-occurrence of audiovisual events in videos can be exploited to localize objects over the image field by solely monitoring the sound in the environment. Thus far, this has only been feasible in scenarios where the camera is static and for single object detection. Moreover, the robustness of these methods has been limited as they primarily rely on RGB images which are highly susceptible to illumination and weather changes. In this work, we present the novel self-supervised MM-DistillNet framework consisting of multiple teachers that leverage diverse modalities including RGB, depth and thermal images, to simultaneously exploit complementary cues and distill knowledge into a single audio student network. We propose the new MTA loss function that facilitates the distillation of information from multimodal teachers in a self-supervised manner. Additionally, we propose a novel self-supervised pretext task for the audio student that enables us to not rely on labor-intensive manual annotations. We introduce a large-scale multimodal dataset with over 113,000 time-synchronized frames of RGB, depth, thermal, and audio modalities. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods while being able to detect multiple objects using only sound during inference and even while moving.