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Occluded Object Detection Using Segmentation
We propose detection of an object whether it is covered by any obstacle or clearly visible. We propose a solution using a combination of Deep-Mask and YOLOv2. Depth map is used for relative distances
O
Occluded Object Detection Using Segmentation
We propose detection of an object whether it is covered by any obstacle or clearly visible. We propose a solution using a combination of Deep-Mask and YOLOv2. Depth map is used for relative distances
The problem Occluded Object Detection Using Segmentation solves
This project can be used for following appilcations:
- Relative distance of object from each other (3D Estimation)
- Object Detection (Person detection) in crowded scenarios
- Estimation of object count (For example how many people have attended an event based on crowd images)
Challenges we ran into
The major challenge we ran into was the inavailablility of labelled dataset with occluded objects and their respective depth images. To solve this issue, we used the power of Computer Vision. On clear object with masked labels, we randomly drew rectangles of various colors accross the object thus creating an illusion of occulsion. A black (zeros) rectangle on the mask image at same position gave us the result of corresponding mask.
Another major issue we faced was result of Mask-RCNN led to multiple detection of same object. To overcome this issue, we introduced YOLOv2 network on top of Mask-RCNN network which led to good results for occluded objects.
Technologies used
Discussion