2 research outputs found
Region Graph Based Method for Multi-Object Detection and Tracking using Depth Cameras
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).Comment: Accepted in IEEE Winter Conference in Computer Vision (WACV'16
Identifying Most Walkable Direction for Navigation in an Outdoor Environment
We present an approach for identifying the most walkable direction for
navigation using a hand-held camera. Our approach extracts semantically rich
contextual information from the scene using a custom encoder-decoder
architecture for semantic segmentation and models the spatial and temporal
behavior of objects in the scene using a spatio-temporal graph. The system
learns to minimize a cost function over the spatial and temporal object
attributes to identify the most walkable direction. We construct a new
annotated navigation dataset collected using a hand-held mobile camera in an
unconstrained outdoor environment, which includes challenging settings such as
highly dynamic scenes, occlusion between objects, and distortions. Our system
achieves an accuracy of 84% on predicting a safe direction. We also show that
our custom segmentation network is both fast and accurate, achieving mIOU (mean
intersection over union) scores of 81 and 44.7 on the PASCAL VOC and the PASCAL
Context datasets, respectively, while running at about 21 frames per second