Object Detection and Instance Segmentation

• This assignment focuses on 2D object detection and instance segmentation using the depth data.
• Motivated by the KITTI vision challenge for object detection and tracking, disparity map between the corresponding images from left (p2) and right (p3) camera are provided.
• Depth map for all images are estimated using the given disparity and calibration information, and the results are attached along the submission as advised.
• An off-the-shelf configured 2D object detection algorithm YOLO v3 pre-trained on COCO dataset is provided for direct implementation.
• By tuning the confidence and non-maxima suppression threshold values, all cars in the frame of left image are detected and attached in this report.
• For each detection of objects with label ‘car’, the average depth is calculated neglecting the values with zero depth.
• Then a heuristic way for instance segmentation is implemented, to calculate the range of depth values to be considered as that of car, based on the average depth.
• More detailed information regarding the implementation is provided under each corresponding section.

NOTE: Please upgrade to the latest OpenCV version to avoid errors.

1. Kindly create new folders inorder to save results.

   • data/test/est_depth
   • data/test/yolo
   • data/test/est_segmentation

2. The bounding boxes are saved as numpy arrays and accessed later during the instance segmentation process. So it’s imperative to follow step 1.