9,644 research outputs found
Forensic Video Analytic Software
Law enforcement officials heavily depend on Forensic Video Analytic (FVA)
Software in their evidence extraction process. However present-day FVA software
are complex, time consuming, equipment dependent and expensive. Developing
countries struggle to gain access to this gateway to a secure haven. The term
forensic pertains the application of scientific methods to the investigation of
crime through post-processing, whereas surveillance is the close monitoring of
real-time feeds.
The principle objective of this Final Year Project was to develop an
efficient and effective FVA Software, addressing the shortcomings through a
stringent and systematic review of scholarly research papers, online databases
and legal documentation. The scope spans multiple object detection, multiple
object tracking, anomaly detection, activity recognition, tampering detection,
general and specific image enhancement and video synopsis.
Methods employed include many machine learning techniques, GPU acceleration
and efficient, integrated architecture development both for real-time and
postprocessing. For this CNN, GMM, multithreading and OpenCV C++ coding were
used. The implications of the proposed methodology would rapidly speed up the
FVA process especially through the novel video synopsis research arena. This
project has resulted in three research outcomes Moving Object Based Collision
Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and
Tampering Detection Inter-Frame Forgery.
The results include forensic and surveillance panel outcomes with emphasis on
video synopsis and Sri Lankan context. Principal conclusions include the
optimization and efficient algorithm integration to overcome limitations in
processing power, memory and compromise between real-time performance and
accuracy.Comment: The Forensic Video Analytic Software demo video is available
https://www.youtube.com/watch?v=vsZlYKQxSk
Moving Object Based Collision-Free Video Synopsis
Video synopsis, summarizing a video to generate a shorter video by exploiting
the spatial and temporal redundancies, is important for surveillance and
archiving. Existing trajectory-based video synopsis algorithms will not able to
work in real time, because of the complexity due to the number of object tubes
that need to be included in the complex energy minimization algorithm. We
propose a real-time algorithm by using a method that incrementally stitches
each frame of the synopsis by extracting object frames from the user specified
number of tubes in the buffer in contrast to global energy-minimization based
systems. This also gives flexibility to the user to set the threshold of
maximum number of objects in the synopsis video according his or her tracking
ability and creates collision-free summarized videos which are visually
pleasing. Experiments with six common test videos, indoors and outdoors with
many moving objects, show that the proposed video synopsis algorithm produces
better frame reduction rates than existing approaches.Comment: The summarized output videos are available at
https://anton-jeran.github.io/M2SYN
LOCALIZED TEMPORAL PROFILE OF SURVEILLANCE VIDEO
Surveillance videos are recorded pervasively and their retrieval currently still relies on human operators. As an intermediate representation, this work develops a new temporal profile of video to convey accurate temporal information in the video while keeping certain spatial characteristics of targets of interest for recognition. The profile is obtained at critical positions where major target flow appears. We set a sampling line crossing the motion direction to profile passing targets in the temporal domain. In order to add spatial information to the temporal profile to certain extent, we integrate multiple profiles from a set of lines with blending method to reflect the target motion direction and position in the temporal profile. Different from mosaicing/montage methods for video synopsis in spatial domain, our temporal profile has no limit on the time length, and the created profile significantly reduces the data size for brief indexing and fast search of video
A Smart Browsing System with Colour Image Enhancement for Surveillance Videos
Surveillance cameras have been widely installed in large cities to monitor and record human activities for different applications. Since surveillance cameras often record all events 24 hours/day, it necessarily takes huge workforce watching surveillance videos to search for specific targets, thus a system that helps the user quickly look for targets of interest is highly demanded. This paper proposes a smart surveillance video browsing system with colour image enhancement. The basic idea is to collect all of moving objects which carry the most significant information in surveillance videos to construct a corresponding compact video by tuning positions of these moving objects. The compact video rearranges the spatiotemporal coordinates of moving objects to enhance the compression, but the temporal relationships among moving objects are still kept. The compact video can preserve the essential activities involved in the original surveillance video. This paper presents the details of browsing system and the approach to producing the compact video from a source surveillance video. At the end we will get the compact video with high resolution.
DOI: 10.17762/ijritcc2321-8169.15038
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