Our this article is on this research paper . For more click here .
Abstract of the paper
People detection methods are highly sensitive to the perpetual occlusion among the targets . As multi-camera set-ups become more frequently encountered , joint exploitation of the across views information would allow for improved detection performances . We provides a large-scale HD dataset named WILDTRACK which finally makes advanced deep learning methods applicable to this problem .
In summary , we overview existing , multi-camera datasets and detection methods , enumerate details of our dataset , and we benchmark multi-camera state of the art detectors on this new dataset .
Introduction
Pedestrian detection is sub-category of object detection . Despite the remarkable recent advances , notably lately owning to the integration of the deep learning methods , the performance of these monocular detectors remains limited to medium level occluded applications at the maximum . This statement is legitimate , since given the monocular observation , the underlying cause , in our case the persons to identify , under highly occluded scenes is ambiguous .
Genuinely, multi-camera detectors come at hand . In general , simple averaging of the per-view predictions , can only improve upon a single view detector . Further, more sophisticated methods jointly make use of the information to yield a prediction .
In summary :
1. Provided dataset larger scale HD dataset advantages are :
-Multi-View detection
-Monocular detection
-Camera calibration
2. We provide experimental benchmark results on this dataset of state of the art multi-camera detection methods .
3. We give an overview of the existing methods and datasets and we discuss research directions .
https://www.epfl.ch/labs/cvlab/data/data-wildtrack/
No comments:
Post a Comment