Obstacle Detection through Forward MovementQiuwei Shou and Alan Wu, CSCI-‐B657 Computer Vision, Indiana University
• Obstacle detection and avoidance are essential capabilities for autonomous robots (land and air)
• While traditional multi-‐sensory approach addresses obstacle detection, the use of a single (monocular) camera enables a more affordable solution
• Computational requirements must be manageable by embedded controllers
• Project is inspired by [Mori and Scherer, 2013]1 paper
• We manually assembled our image dataset, which is primarily derived from drone hobbyist videos taken from a first-‐person view (FPV), downloaded from YouTube
• Mostly forest scenes with various levels of luminance and tilt angles throughout flight
• Two datasets totaling ~500 previous/current frame combinations
• Set 1: ~400 dense forest• Set 2: ~100 open field (no obstacles)
Feature Detection
Feature Filtering
Template Extraction Feature Matching
Obstacle Detection Algorithm
Previous Image
Obstacle: Yes or No
Current Image
• We divide each image into 4x4 regions and use SURF for fast feature detection• Only certain features in the “previous” frame are selected for matching with
“current” frame• Features must be of a minimum size• Features must be within certain regions of the image
• We then perform template matching1 to detect approaching object(s) that may be obstacles. See upper-‐right of poster for details• Initial template window size W affects detection accuracy as seen in graph below
Motivation
Dataset
Methodology
Current ImagePrevious Image
Sample Obstacle Dataset
Sample Non-‐obstacle Dataset
FeatureFiltering
TemplateExtraction
Challenges• Dataset is largely “uncalibrated” – frame captures estimated to
contain obstacles, but not guaranteed
• SURF is not optimal with noisy background
• Often unable to differentiate features on the ground from protruding objects (such as trees), especially when the camera is tilted downward
References: 1Mori, Tomoyuki and Sebastian Scherer. “First Results in Detecting and Avoiding Frontal Obstacles from a Monocular Camera for Micro Unmanned Aerial Vehicles.” ICRA, 2013.
2http://www.ingeniousingenuity.com3http://clipartion.com/
2
3
Conclusion and Future Work• Able to detect oncoming obstacles using a low computation
algorithm based on SURF features and template matching
• Access textural library (ex. tree bark) for more targeted detection and matching; possibly combine with Visual SLAM to achieve higher fidelity in estimating obstacle depth
Template Matching• Perform template matching1 to determine scale increase of
approaching object• For each remaining feature after f iltering in “previous” frame, a small
window is drawn around feature• A similar window is drawn around matching feature in “current” frame, with
window size scaled up by K; resize window in “previous” frame also by K• Scalar K is determined such that the correlation distance (SSD) between
“previous” and “current” windows is minimized. Discard any matches with distance D > Dmax and K < Kmin
Previous Frame Feature Current Frame Feature
1. Scale window size by K
2. Resize feature window by K
3. Calculate distance for various K
Results• We achieved an accuracy of 84% (i.e. False Neg = 16%) in detecting
obstacles in Set 1 (dense forest) and a False Pos rate of 33% in falsely detecting obstacles in Set 2 (open field), for initial window size of 30 and ¼ resolution relative to original (72 ppi)
• Plots show effects of initial window size W and image resolution
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
10 15 20 25 30 35 40 45 50
False
-Rate
Template-Window-Size
Template-Window-Size-vs.-Detection-Accuracy-on-Baseline-Dataset
False-Neg
False-Pos
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
20 25 30 35 40
False/Ra
te
Template/Window/Size
Template/Window/Size/vs./Detection/Accuracy,/0.25x/ppi/
False/Neg
False/Pos
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5
False
.Rate
Resolution. re:.Original
Resolution.vs..Detection.Accuracy,.W.=.30
False.Neg
False.Pos
Recommended
