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A Change Information Based Fast Algorithm for Video Object Detection and Tracking
Post: #1

A Change Information Based Fast Algorithm for Video Object Detection and Tracking

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DETECTION and tracking of moving objects from a
video scene is a challenging task in video processing
and computer vision [1]–[4]. It has wide applications such
as video surveillance, event detection, activity recognition,
activity based human recognition, fault diagnosis, anomaly

detection, robotics, autonomous navigation, dynamic scene
analysis, path detection, and others [1]–[4]. Moving object
detection in a video is the process of identifying different
object regions which are moving with respect to the background.
More specifically, moving object detection in a video
is the process of identifying those objects in the video whose
movements will create a dynamic variation in the scene [2].
This can be achieved by two different ways: 1) motion detection/
change detection, and 2) motion estimation [2]. Change
or motion detection is the process of identifying changed and
unchanged regions from the extracted video image frames
when the camera is fixed and the objects are moving.

Proposed Algorithm for Object Detection

A block diagrammatic representation of the proposed
scheme is given in Fig. 1. Here we use two types of segmentation
schemes: one is a spatio-temporal spatial segmentation
and the other is a temporal segmentation. Spatial segmentation
helps in determining the boundary of the regions in the scene
accurately, and temporal segmentation helps in determining
the foreground and the background parts of it.

Spatio-Temporal Spatial Segmentation

In the spatio-temporal spatial segmentation scheme, we
have modeled each video image frame with compound MRF
model and the segmentation problem is solved using the
MAP estimation principle. For initial frame segmentation, a
hybrid algorithm is proposed to obtain the MAP estimate. For
segmentation of other frames, changes between the frames is
imposed on the previously available segmented frame so as
to have an initialization to find the segmentation result of
other frames. The total scheme is described in detail in the
subsequent sections.

Conclusion and Discussion
In this article, a change information based moving object
detection scheme is proposed. The spatio-temporal spatial
segmentation result of the initial frame is obtained by edgebased
MRF modeling and a hybrid MAP estimation algorithm
(hybrid of SA and ICM). The segmentation result of the initial
frame together with some change information from other
frames is used to generate an initialization for segmentation
of other frames. Then, an ICM algorithm is used on that
frame starting from the obtained initialization for segmentation.
It is found that the proposed approach produces better
segmentation results compared to those of edgeless and JSEG
segmentation schemes and comparable results with edgebased
Post: #2
The videos are actually sequences of images, each of which is called a frame, which is displayed at a frequency fast enough so that human eyes can perceive the continuity of its contents. It is obvious that all image processing techniques can be applied to individual frames. In addition, the contents of two consecutive frames are often closely related.
Visual content can be modeled as a hierarchy of abstractions. At the first level are the raw pixels with color or brightness information. Additional processing produces features such as edges, corners, lines, curves, and color regions. A higher abstraction layer can combine and interpret these features as objects and their attributes. At the highest level are the human-level concepts that involve one or more objects and relationships between them.

The detection of objects in videos consists of verifying the presence of an object in sequences of images and possibly locate it precisely for its recognition. Object tracking is monitoring the spatial and temporal changes of an object during a video sequence, including its presence, position, size, shape, and so on. This is done by solving the problem of time correspondence, the problem of matching the target region in successive frames of a sequence of images taken at closely spaced intervals of time. These two processes are closely related because tracking usually begins with object detection, while detecting an object repeatedly in the subsequent image sequence is often necessary to assist and verify follow-up.

The MRF-MAP framework is computationally intensive due to random initialization. To reduce this load, we propose a heuristic initialization technique based on change information. The scheme requires an initially segmented framework. For the initial segmentation of the frame, the composite MRF model is used to model attributes and the MAP estimation is obtained by a hybrid algorithm [simulation annealing (SA) and iterative conditional mode (ICM)] that converges rapidly. For temporal segmentation, instead of using a gray level difference (CDM) based change detection mask, we propose a CDM based on the difference of two frame tags. The proposed scheme resulted in less effect of the silhouette. In addition, a combination of spatial and temporal segmentation is used to detect moving objects. The results of the proposed spatial segmentation approach are compared with those of the JSEG method, and the segmentation approaches without borders or edges. It is observed that the proposed approach provides better spatial segmentation compared to the other three methods.

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