Region Graph Based Method for Multi-Object Detection and Tracking using Depth Cameras
This addresses noisy depth data in multi-object tracking for applications like robotics or surveillance, but appears incremental as it builds on existing region and graph-based approaches.
The paper tackles the problem of noisy depth maps in multi-object detection and tracking by proposing a region-based method to suppress high magnitude noise and using temporal learning for region detection with weighted graph-based tracking. Experimental results show the method successfully suppresses noise and detects/tracks objects with and without occlusion on standard datasets.
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).