Moving Object Detection from Mobile Platforms Using Stereo Data Registration - Computational Intelligence Paradigms in Advanced Pattern Classification - page 28

On the contrary to the previous approaches, [36] proposes a fast registration
method based on solving an energy minimization problem derived from an implicit
polynomial fitted to the given model set [37]. This IP is used to define a gradient flow
that drives the data set to the model set without using point-wise correspondences.
The energy functional is minimized by means of a heuristic two step process. Firstly,
every point in the given data set moves freely along the gradient vectors defined by
the IP. Secondly, the outcome of the first step is used to define a single transfor-
mation that represents this movement in a rigid way. These two steps are repeated
alternately until convergence is reached. The weak point of this approach is the first
step of the minimization that lets the points move independently in the proposed gra-
dient flow. Furthermore, the proposed gradient flow is not smooth, specially close
to the boundaries.
Most of the algorithms presented above have been originally proposed for reg-
istering overlapped sets of points corresponding to the 3D surface of a single rigid
object. Extensions to a more general framework, where the 3D surfaces to be reg-
istered correspond to different views of a given scene, have been presented in the
robotic field (e.g., [30, 18]). Actually, in all these extensions, the registration is used
for the simultaneous localization and mapping ( SLAM ) of the mobile platform (i.e.,
the robot). Although some approaches differentiate static and dynamic parts of the
environment before registration (e.g., [30], [33]), most of them assume that the en-
vironment is static, containing only rigid, non-moving objects. Therefore, if moving
objects are present in the scene, the least squares formulation of the problem will
provide a rigid transformation biased by the motions in the scene.
Independently to the kind of scenario to be tackled (partial view of a single object
or whole scene), 3D registration algorithms are computationally expensive, which
prevents their use in real time applications. In the current work a robust strategy that
reduces the CPU time by focusing only on feature points is proposed. It is intended to
be used in ADAS (Advanced Driver Assistance Systems) applications, in which an
on-board camera explores the current scene in real time. Usually, an exhaustive win-
dow scanning approach is adopted to extract regions of interests (ROIs), needed in
pedestrian or vehicle detection systems. The concept of consecutive frame registra-
tion for moving object detection has been explored in [11], in which an active frame
subtraction for pedestrian detection from images of moving cameras is proposed. In
that work, consecutive frames were not registered by a vision based approach but
by estimating the relative camera motion using vehicle speed and a gyrosensor. A
similar solution has been proposed in [15], but by using GPS information.

3 Proposed Approach

The proposed approach combines 2D detection of key points with 3D registration.
The first stage consists in extracting a set of 2D feature points at a given frame
and track it through the next frame; 3D coordinates corresponding to each of these
2D feature points are later on used during the registration process, where the rigid
displacement (six degrees of freedom) that maps the 3D scene associated with frame