Multiclass Multimodal
Detection and Tracking in Urban Environments
L. Spinello , R. Triebel
and R.
Siegwart
International Journal of
Robotics Research (IJRR)
Keywords: multimodal people and vehicle
detection, camera and laser people and car detection, sensor fusion,
ISMe, Conditional Random Fields
This
paper presents a
novel approach to detect and track people and cars based on
the combined information retrieved from a camera and a laser range
scanner. Laser data points are classified by using boosted Conditional
Random Fields (CRF), while the image based detector uses an extension
of the Implicit Shape Model (ISM), which learns a codebook of
local descriptors from a set of hand-labeled images and uses
them to vote for centers of detected objects. Our extensions
to ISM include the learning of object parts and template masks to
obtain more distinctive votes for the particular object classes. The
detections from both sensors are then fused and the objects are tracked
using a Kalman Filter with multiple motion models. Experiments
conducted in real-world urban scenarios demonstrate the effectiveness
of our approach.
Multiclass
Multimodal Detection and Tracking in Urban Environments