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

03/11/2016
by   Sachin Mehta, et al.
0

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).

READ FULL TEXT

page 2

page 6

page 7

research
02/05/2018

Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques

It is an important task to reliably detect and track multiple moving obj...
research
04/04/2013

Shadow Detection: A Survey and Comparative Evaluation of Recent Methods

This paper presents a survey and a comparative evaluation of recent tech...
research
03/05/2021

Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection

The ability to accurately detect and localize objects is recognized as b...
research
05/08/2015

Noise in Structured-Light Stereo Depth Cameras: Modeling and its Applications

Depth maps obtained from commercially available structured-light stereo ...
research
12/20/2020

Enabling Tangible Interaction through Detection and Augmentation of Everyday Objects

Digital interaction with everyday objects has become popular since the p...
research
12/10/2022

Source-free Depth for Object Pop-out

Depth cues are known to be useful for visual perception. However, direct...
research
04/15/2022

Towards PAC Multi-Object Detection and Tracking

Accurately detecting and tracking multi-objects is important for safety-...

Please sign up or login with your details

Forgot password? Click here to reset