Region Graph Based Method for Multi-Object Detection and Tracking using Depth Cameras

TL;DR

This paper introduces a novel region graph-based approach for multi-object detection and tracking using noisy depth camera data, effectively handling occlusions and high-magnitude noise through region suppression and temporal learning.

Contribution

The paper presents a new method combining region-based noise suppression with temporal learning and graph-based tracking specifically designed for depth camera data.

Findings

Effective noise suppression in depth maps

Accurate detection and tracking of objects with occlusions

Robust performance demonstrated on standard datasets

Abstract

In this paper, we propose a multi-object detection and tracking method using depth cameras. Depth maps are very noisy and obscure in object detection. We first propose a region-based method to suppress high magnitude noise which cannot be filtered using spatial filters. Second, the proposed method detect Region of Interests by temporal learning which are then tracked using weighted graph-based approach. We demonstrate the performance of the proposed method on standard depth camera datasets with and without object occlusions. Experimental results show that the proposed method is able to suppress high magnitude noise in depth maps and detect/track the objects (with and without occlusion).