Information Technology Reference
In-Depth Information
[26]; aerial surveillance [35] [34]; video segmentation [1]; vehicles and driver assis-
tance [15], [24]; just to mention a few. As mentioned above, the underlying strategy
in the solutions proposed in the literature essentially relies on the compensation of
the camera motion. The difference between them lie on the sensor (i.e., monocu-
lar/stereoscopic) or on the use of prior-knowledge of the scene together with visual
cues. For instance, [26] uses a stereo system and predicts the depth image for the
current time by using ego-motion information and the depth image obtained at the
previous time. Then, moving objects are easily detected by comparing the predicted
depth image with the one obtained at the current time. The prior-knowledge of the
scene is also used in [35] and [34]. In these cases the authors assume that the scene
is far from the camera (monocular) and the depth variation of the objects of interest
is small compared to the distance (e.g., airborne image sequences). In this context
camera motion can be approximately compensated by a 2D parametric transforma-
tion (a 3x3 homography). Hence, motion compensation is achieved by warping a
sequence of frames to a reference frame, where moving objects are easily detected
by image subtraction like in the stationary camera cases.
A more general approach has been proposed in [1] for segmenting videos cap-
tured with a freely moving camera, which is based on recording complex back-
ground and large moving non-rigid foreground objects. The authors propose a
region-based motion compensation. It estimates the motion of the camera by find-
ing the correspondence of a set of salient regions obtained by segmenting successive
frames. In the vehicle on-board vision systems and driver assistance fields, the com-
pensation of camera motion has also attracted researchers' attention in recent years.
For instance, in [15] the authors present a simple but effective approach based on
the use of GPS information to roughly align frames from video sequences. A lo-
cal appearance comparison between the aligned frames is used to detect objects. In
the driver assistance context, but by using an onboard stereo rig, [24] introduce a
3D data registration based approach to compensate camera motion from two con-
secutive frames. In that work, consecutive stereo frames are aligned into the same
coordinate system; then moving objects are obtained from a 3D frame subtraction,
similar to [26]. The current chapter proposes an extension of [24], by detecting mis-
registration regions according to an adaptive threshold from the depth information.
The remainder of this chapter is organized as follows. Section 2 introduces related
work in the 3D data registration problem. Then, Section 3 presents the proposed ap-
proach for moving object detection. It consists of three stages:
i
)
2D feature point
detection and tracking;
ii
moving object de-
tection through consecutive stereo frame subtraction. Experimental results in real
environments are presented in Section 4. Finally, conclusions and future works are
giveninSection5.
)
robust 3D data registration; and
iii
)
2
Related Work
A large number of approaches have been proposed in the computer vision commu-
nity for 3D Point registration during the last two decades (e.g., [3], [4], [22]). 3D
Search WWH ::
Custom Search