In recent years due to various kinds of social activities such as theft, bomb attack and other terrorist attack preventive
security measures at public places has gained lot of importance. Abandoned Object detection is most crucial task in visual
surveillance system. Many Public or open areas are facilitated with cameras at the multiple angles to monitor the security of that area
for keeping citizens safe. This is known as the surveillance system. In this paper a new algorithm is proposed for object tracking in
video, which is based on image segmentation. With the image segmentation all objects in video can be detected whether they are
moving or not by using segmentation results of successive frames. This approach definitely provides security and detects the moving
object in a real time video sequence and live video streaming. Attempt of this paper is to study all the different techniques available
for abandoned detection and to find out the research possibilities available in this area.
Komal Sunil Patil : ME 2nd Year, North Maharashtra University,
SSGB Engineering & Technology, Bhusawal, Maharashtra.
Girish A. Kulkarni : HOD of E&Tc, North Maharashtra University,
SSGB Engineering & Technology, Bhusawal, Maharashtra.
Abandoned luggage detection, abandoned object detection, object detection and tracking, video surveillance, left
baggage detection, background subtraction.
1) There are different techniques that can be available
for abandoned object detection out of Background
subtraction, Blob Detection and Tracking, Morphological
Processing which are useful.
2) There are different techniques of abandoned object
detection by using short term and long term
parameters.
3) Problem formulation can be done by identifying
various methods\techniques such as Background
Modeling and Subtraction, Foreground Analysis, Blob
Extraction etc.
4) The temporal consistency model is described by a
very simple FSM. It exploits the temporal transition
pattern generated by short-term and long-term
background models, which can accurately identify
static foreground objects.
5) The back-tracing algorithm tracks the luggage
owner by using spatial-temporal windows to efficiently
verify left-luggage events.
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