Abstract: Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.
Abstract: Unintentional falls are rife throughout the ages and
have been the common factor of serious or critical injuries especially
for the elderly society. Fortunately, owing to the recent rapid
advancement in technology, fall detection system is made possible,
enabling detection of falling events for the elderly, monitoring the
patient and consequently provides emergency support in the event of
falling. This paper presents a review of 3 main categories of fall
detection techniques, ranging from year 2005 to year 2010. This
paper will be focusing on discussing the techniques alongside with
summary and conclusion for them.
Abstract: Robustness is one of the primary performance criteria for an Intelligent Video Surveillance (IVS) system. One of the key factors in enhancing the robustness of dynamic video analysis is,providing accurate and reliable means for shadow detection. If left undetected, shadow pixels may result in incorrect object tracking and classification, as it tends to distort localization and measurement information. Most of the algorithms proposed in literature are computationally expensive; some to the extent of equalling computational requirement of motion detection. In this paper, the homogeneity property of shadows is explored in a novel way for shadow detection. An adaptive division image (which highlights homogeneity property of shadows) analysis followed by a relatively simpler projection histogram analysis for penumbra suppression is the key novelty in our approach.
Abstract: Motion detection is very important in image
processing. One way of detecting motion is using optical flow.
Optical flow cannot be computed locally, since only one independent
measurement is available from the image sequence at a point, while
the flow velocity has two components. A second constraint is needed.
The method used for finding the optical flow in this project is
assuming that the apparent velocity of the brightness pattern varies
smoothly almost everywhere in the image. This technique is later
used in developing software for motion detection which has the
capability to carry out four types of motion detection. The motion
detection software presented in this project also can highlight motion
region, count motion level as well as counting object numbers. Many
objects such as vehicles and human from video streams can be
recognized by applying optical flow technique.