1. Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
NEERAJ KANHERE, SHRINIVAS PUNDLIK, AND STAN BIRCHFIELD
CLEMSON UNIVERSITY
ABSTRACT
SELECTING STABLE FEATURES
EXPERIMENTAL RESULTS
Steps:
Background subtraction
Feature projection
Feature height estimation from two-level homography
We present a novel method for visually monitoring a highway when the camera is relatively low to the ground and on the side of the road. In such a case, occlusion and the perspective effects due to the heights of the vehicles cannot be ignored. Features are detected and tracked throughout the image sequence, and then grouped together using a multilevel homography, which is an extension of the standard homography to the low-angle situation. We derive a concept called the relative height constraint that makes it possible to estimate the 3D height of feature points on the vehicles from a single camera, a key part of the technique. Experimental results on several different highways demonstrate the system’s ability to successfully segment and track vehicles at low angles, even in the presence of severe occlusion and significant perspective changes.
Results are shown for three freeway sequences. Vehicles are grouped correctly despite severe occlusion.
q', p0', and pM': 2x1 vectors in (x,y) plane
M: reference height for the top-level homography.
Select stable features:
Height estimates are consistent over several frames
Mean height estimate is less than a threshold (close to ground)
THE PROBLEM
REFINING HEIGHT ESTIMATES
Sequence 1, Frame 25-35
Sequence 2, Frame 60-70
Sequence 3, Frame
High-angle, on-axis view (typical scenario)
Low-angle, off-axis view
(our scenario)
Assumptions: flat road and translational motion model (reasonable for freeways)
Relative Height Constraint
Estimate height of unstable feature p from stable feature q (both on the same rigid body undergoing translation)
Sequence 3, Frame
Sequence 3, Frame
Sequence 3, Frame
Each stable feature yields a height estimate
Choose estimate that minimizes
Euclidean distance between stable and unstable feature
Absolute trajectory error between them
CONCLUSION
Multi-level homography is used to segment and track vehicles from a low angle
Despite severe occlusion, feature points are grouped correctly
Resultant trajectories are in 3D world coordinates
Future work:
Classifying vehicles based upon size
Handling complete occlusions, rotation
Using shape and structural constraints for more accurate height estimates
GROUPING FEATURES
Features are grouped using Normalized Cuts
direction of travel
direction of travel
Affinity Matrix
Cars are well-separated (little to no occlusion)
Can assume that all motion is in the road plane (single homography is effective)
Cars overlap significantly (lots of occlusion)
3D effects are important (must take the height of vehicles into account)
Measures foreground connectivity between features i and j
Measures absolute trajectory error between features i and j
Measures Euclidean distance between features i and j